Beyond Accuracy: A Comprehensive Guide to Performance Metrics for Cancer Classification Models

Abstract

This article provides a comprehensive guide to performance metrics for cancer classification models, tailored for researchers, scientists, and drug development professionals. It covers foundational concepts like the confusion matrix, accuracy, precision, recall, and F1-score, and explores their application in complex, real-world scenarios such as multi-omics data integration and multi-class cancer subtype classification. The guide addresses critical challenges including class imbalance and the precision-recall trade-off, and outlines robust validation methodologies and statistical testing for comparative model analysis. By synthesizing these elements, the article aims to equip professionals with the knowledge to select, interpret, and validate metrics that align with both clinical imperatives and research objectives in oncology.

Core Concepts: Understanding the Basic Metrics for Cancer Classification

In the high-stakes field of cancer classification research, the accurate evaluation of diagnostic models is paramount. For researchers, scientists, and drug development professionals, a model's predictive performance directly influences clinical insights and potential patient outcomes. Among the available evaluation tools, the confusion matrix stands as the fundamental, interpretable cornerstone for assessing binary classification models. It provides a detailed breakdown of a model's predictions versus actual outcomes, forming the basis for critical metrics like sensitivity and precision. This guide explores the architecture of the confusion matrix, its derived metrics, and their practical application in cancer diagnostics, providing a structured framework for objective model comparison.

Demystifying the Confusion Matrix: A Detailed Breakdown

A confusion matrix, sometimes called an error matrix, is a specific table layout that visualizes the performance of a classification algorithm [1]. It moves beyond simple accuracy by providing a granular view of where a model succeeds and, crucially, where it becomes "confused" [2] [1].

In its simplest form for binary classification, the matrix is a 2x2 table that cross-references the actual conditions with the predicted conditions, creating four distinct outcomes [3] [2] [4]:

• True Positive (TP): The model correctly predicts the positive class. In a cancer context, this is a patient with cancer correctly identified as having cancer [1].
• True Negative (TN): The model correctly predicts the negative class. This is a healthy patient correctly identified as not having cancer [1].
• False Positive (FP): The model incorrectly predicts the positive class. This is a healthy patient wrongly flagged as having cancer—a Type I error [3] [5].
• False Negative (FN): The model incorrectly predicts the negative class. This is a patient with cancer who is missed by the model—a Type II error [3] [5].

The following diagram illustrates the logical relationship between these components and the key metrics derived from them.

Key Metrics Derived from the Confusion Matrix

The raw counts within the confusion matrix are used to calculate powerful metrics that evaluate model performance from different perspectives. The choice of which metric to prioritize depends heavily on the specific clinical or research objective [5].

Metric	Formula	Clinical Interpretation in Cancer Diagnostics
Accuracy	(TP + TN) / Total [3] [5]	The overall proportion of correct diagnoses. Can be misleading if the dataset is imbalanced [5] [1].
Recall (Sensitivity)	TP / (TP + FN) [3] [5]	The model's ability to correctly identify all patients who actually have cancer. Critical for minimizing missed diagnoses [2] [5].
Precision	TP / (TP + FP) [3] [5]	The accuracy of the model's positive predictions. Important when the cost of false alarms (unnecessary biopsies) is high [2] [5].
Specificity	TN / (TN + FP) [3] [6]	The model's ability to correctly identify healthy patients. The inverse of the False Positive Rate [2] [4].
F1-Score	2 * (Precision * Recall) / (Precision + Recall) [3] [5]	The harmonic mean of precision and recall. Provides a single balanced metric for imbalanced datasets [3] [5].

Experimental Protocols & Data Presentation in Cancer Research

To illustrate the practical application of these metrics, consider the following experimental data synthesized from recent studies on cancer classification models. The table below provides a comparative analysis of different AI model architectures, highlighting their performance across key confusion matrix-derived metrics.

Table 1: Comparative Performance of Recent Cancer Classification Models

Model / Study	Cancer Focus	Data Modality	Accuracy	Recall (Sensitivity)	Precision	Specificity
DenseNet121 with Multi-Scale Feature Fusion [7]	Breast Cancer	Histopathological Images (BreakHis)	97.1%	92.0% (Malignant)	Not Reported	93.8% (Benign)
Stacked Deep Learning Ensemble [8]	Multi-Cancer (5 types)	Multiomics (RNA-seq, Methylation)	98.0%	Implied High	Implied High	Implied High
ResNet-SVM Hybrid [7]	Breast Cancer	Mammogram & Ultrasound Fusion	99.22%	Not Reported	Not Reported	Not Reported
Optimized Bayesian CNN (OBCNN) [7]	Breast Cancer (IDC)	Histopathology Images	Not Reported	Demonstrated Robustness	Demonstrated Robustness	Demonstrated Robustness

Detailed Experimental Methodology

The performance metrics in Table 1 are the result of rigorous experimental protocols. The following diagram outlines a generalized workflow for developing and evaluating a cancer classification model, from data preparation to performance validation.

Key Experimental Steps:

• Data Collection & Curation: Models are trained on curated biomedical datasets. For example, the DenseNet121 model for breast cancer used the publicly available BreaKHis dataset of histopathological images [7], while the multiomics ensemble model utilized RNA sequencing and methylation data from The Cancer Genome Atlas (TCGA) [8].
• Data Preprocessing: This critical step ensures data quality and model stability. It includes:
  • Normalization: Techniques like Transcripts Per Million (TPM) for RNA-seq data to remove technical bias [8].
  • Feature Extraction: Using methods such as autoencoders to reduce the high dimensionality of omics data while preserving biologically relevant information [8].
• Model Training with Cross-Validation: To ensure robustness and avoid overfitting, models are often trained using techniques like 5-fold cross-validation, especially on limited medical data [7].
• Performance Evaluation: Predictions on a held-out test set are compiled into a confusion matrix, which is then used to calculate the final performance metrics [7] [8].

The Scientist's Toolkit: Essential Research Reagents & Solutions

The development of high-performing cancer classification models relies on a suite of computational "reagents" and datasets. The table below details these essential components and their functions.

Table 2: Key Research Reagent Solutions for Cancer Classification Models

Item Name	Category	Function / Description
The Cancer Genome Atlas (TCGA)	Datasets	A comprehensive public dataset containing molecular characterization and clinical data from over 20,000 primary cancer samples across 33 cancer types [8].
BreaKHis	Datasets	A public dataset of histopathological breast cancer biopsy images, used for developing and testing image-based classification models [7].
Convolutional Neural Network (CNN)	Algorithm	A deep learning architecture highly effective for analyzing image data (e.g., histopathological slides, mammograms) by learning spatial hierarchies of features [7] [8].
Stacking Ensemble	Algorithm	A advanced technique that combines multiple machine learning models (e.g., SVM, RF, CNN) using a meta-learner to improve overall predictive performance and robustness [8].
Autoencoder	Tool	A neural network used for unsupervised feature extraction and dimensionality reduction, crucial for handling high-dimensional omics data [8].
Synthetic Minority Over-sampling Technique (SMOTE)	Tool	An algorithm used to address class imbalance in datasets by generating synthetic samples of the underrepresented class, preventing model bias [8].

The confusion matrix is an indispensable tool for objectively evaluating binary classification models in cancer research. By providing a detailed breakdown of a model's predictive behavior, it enables researchers to move beyond simplistic accuracy and understand the true diagnostic capabilities of their models. The derived metrics—recall, precision, specificity, and F1-score—each tell a different part of the story, allowing scientists to select and optimize models based on specific clinical priorities, whether that is minimizing deadly false negatives or reducing costly false positives. As AI continues to integrate into biomedical research and diagnostics, the rigorous, metric-driven framework provided by the confusion matrix will remain the foundation for validating and comparing the performance of these powerful tools.

In the development of cancer classification models, the selection of appropriate performance metrics is not merely a technical formality but a foundational aspect of clinical relevance and model utility. Models that distinguish malignant from benign tissues or classify cancer subtypes must be evaluated beyond simple correctness, as the real-world costs of different types of errors—missing a cancer versus raising a false alarm—are profoundly asymmetric [5] [9]. For researchers, scientists, and drug development professionals, understanding the trade-offs encapsulated by accuracy, precision, recall, and specificity is critical for translating algorithmic predictions into reliable diagnostic tools. This guide provides a comprehensive comparison of these core metrics, grounded in their application to cancer classification research, and supported by experimental data from contemporary studies.

The evaluation of a classification model begins with the confusion matrix, a table that breaks down predictions into four fundamental categories [10] [11]. True Positives (TP) and True Negatives (TN) are cases where the model correctly identifies the positive class (e.g., malignant cancer) and the negative class (e.g., benign), respectively. False Positives (FP) occur when the model incorrectly labels a negative case as positive (a "false alarm"), while False Negatives (FN) occur when it misses a positive case (a "missed detection") [12] [9]. In medical diagnostics, a False Negative in cancer detection is often considered a more severe error than a False Positive, as it could delay life-saving treatment [11] [13].

Metric Definitions and Formulas

Core Definitions and Mathematical Formulations

• Accuracy measures the overall proportion of correct predictions made by the model across both positive and negative classes [5] [14]. It is calculated as: Accuracy = (TP + TN) / (TP + TN + FP + FN) While intuitive, accuracy can be a misleading metric for imbalanced datasets, which are common in medical contexts where the number of healthy patients often far exceeds the number of sick patients. A model that simply always predicts "negative" could achieve high accuracy while being clinically useless [10] [11].

• Precision (also known as Positive Predictive Value) measures the proportion of positive predictions that are actually correct. It answers the question: "When the model predicts cancer, how often is it right?" [5] [12]. It is calculated as: Precision = TP / (TP + FP) High precision indicates that the model is reliable when it flags a case as positive. This is crucial in scenarios where subsequent procedures are costly, invasive, or carry significant psychological burden [10] [9].

• Recall (also known as Sensitivity or True Positive Rate - TPR) measures the model's ability to correctly identify actual positive cases. It answers the question: "Of all the patients who truly have cancer, what fraction did the model successfully find?" [5] [12]. It is calculated as: Recall = TP / (TP + FN) A high recall is paramount in applications like cancer screening, where the cost of missing a disease (a False Negative) is unacceptably high [5] [11].

• Specificity (also known as True Negative Rate - TNR) measures the model's ability to correctly identify actual negative cases. It answers the question: "Of all the patients who are truly healthy, what fraction did the model correctly clear?" [10] [11]. It is calculated as: Specificity = TN / (TN + FP) It is the complement of the False Positive Rate (FPR), which is defined as FPR = 1 - Specificity = FP / (TN + FP) [5] [12].

Visualizing the Relationships

The following diagram illustrates the logical relationships between the core metrics and the confusion matrix components from which they are derived.

Comparative Analysis in Cancer Classification

Performance of Deep Learning Models on Breast Cancer Histopathology

A 2025 study evaluating 11 different deep learning algorithms for classifying breast cancer biopsy images into benign and malignant categories provides a clear comparison of these metrics in a realistic research setting [15]. The models were trained and tested on a dataset of 10,000 images. The table below summarizes the performance of the top-performing model and provides a comparative benchmark.

• Experimental Protocol: The study used a dataset of 10,000 histopathological images (6,172 IDC-negative and 3,828 IDC-positive). The data was split into 80% for training, 10% for validation, and 10% for testing. Various deep learning architectures, including DenseNet201, ResNet50, and VGG16, were trained and evaluated on the held-out test set [15].

Table 1: Performance Metrics of DenseNet201 on Breast Cancer Classification

Model	Accuracy	Precision	Recall	F1-Score	AUC
DenseNet201	89.4%	88.2%	84.1%	86.1%	95.8%

• Result Interpretation: The DenseNet201 model achieved a high overall Accuracy of 89.4%. Its Precision of 88.2% indicates that when it predicts a sample as malignant, it is correct about 88% of the time. The Recall of 84.1% shows it successfully identifies over 84% of all actual malignant cases. The F1-Score (the harmonic mean of precision and recall) of 86.1% reflects a strong balance between the two metrics [15].

Analysis of a Hybrid Ensemble for Skin Cancer Classification

Further insights can be drawn from a 2025 study on skin cancer classification, which proposed a hybrid deep learning ensemble model. The analysis of its confusion matrix offers a granular view of the trade-offs between sensitivity and specificity [13].

• Experimental Protocol: This study utilized a publicly available Kaggle dataset of 10,000 dermoscopic images. The proposed hybrid model combined a CNN-LSTM architecture and a DenseNet-like model, with a Gradient Boosting Classifier acting as a meta-learner to integrate their predictions. Performance was evaluated on a separate test set [13].

Table 2: Confusion Matrix Analysis of a Hybrid Skin Cancer Model (Normalized)

True Label	Predicted: Benign	Predicted: Malignant
Benign	True Negative (TN): 94%	False Positive (FP): 6%
Malignant	False Negative (FN): 11%	True Positive (TP): 89%

Data derived from the normalized confusion matrix of the meta-learner model [13].

• Result Interpretation: From this matrix, key metrics can be derived. The Recall (Sensitivity) is 89% (TP / [TP + FN] = 89% / [89% + 11%]). The Specificity is 94% (TN / [TN + FP] = 94% / [94% + 6%]). This demonstrates a critical balance: the model is highly effective at correctly identifying healthy lesions (high specificity), while also being robust at catching cancerous ones (high recall), with a false negative rate of 11% that was the best among the models compared in the study [13].

The Metric Selection Guide for Cancer Research

Choosing which metric to optimize is a strategic decision driven by the clinical and research context. The following workflow diagrams the decision process for selecting a primary evaluation metric.

Guidance for Researchers

• Prioritize Recall when the primary goal is to identify all positive cases and the cost of a False Negative is high. This is the case in initial cancer screening programs (e.g., mammography, skin cancer checks) where missing a malignant case is unacceptable, and following up on a false alarm is an acceptable trade-off [5] [9]. As one source states, in a scenario checking for a dangerous insect species, it makes sense to maximize recall because "false alarms (FP) are low-cost, and false negatives are highly costly" [5].

• Prioritize Precision when it is critical that positive predictions are highly trustworthy. This is often the case in a second-stage confirmation or when deciding to initiate invasive, costly, or risky treatments (e.g., chemotherapy, surgery). A high precision ensures that patients are not subjected to undue harm and resources are not wasted on false alarms [10] [9].

• Use the F1-Score when you need a single metric to compare models and there is a need to balance both False Positives and False Negatives, especially on imbalanced datasets. The F1-score is the harmonic mean of precision and recall, which penalizes extreme values more than the arithmetic mean, thus providing a more conservative estimate of performance [5] [14].

• Report Specificity alongside Sensitivity when correctly identifying negative cases is a key measure of success. This is important for assessing the overall diagnostic capability of a test and for understanding the rate of false alarms, which can cause patient anxiety and lead to unnecessary follow-up procedures [11] [13].

The Scientist's Toolkit: Essential Research Reagents and Solutions

The following table details key computational tools and methodologies frequently employed in modern cancer classification research, as evidenced by the cited studies.

Table 3: Key Computational Tools in Cancer Classification Research

Tool / Solution	Function in Research	Exemplar Use Case
Convolutional Neural Networks (CNNs)	Automatically extract hierarchical features from medical images for classification.	Used as backbone architectures (e.g., DenseNet, ResNet) in breast and skin cancer image classification [15] [13].
Ensemble Learning & Meta-Learners	Combines predictions from multiple models to improve overall accuracy, robustness, and generalization.	A Gradient Boosting meta-learner was used to combine CNN-LSTM and DenseNet models for skin cancer classification, achieving top performance [13].
Data Augmentation Techniques	Artificially expands the training dataset by applying random transformations (flips, contrast changes) to improve model generalization.	Applied to dermoscopic images to mitigate overfitting and improve model robustness to real-world variation [13].
Stratified Cross-Validation	A resampling procedure that ensures each fold of the data retains the same class distribution as the whole dataset, leading to a more reliable performance estimate.	Crucial for robust model evaluation, particularly with imbalanced medical datasets, to ensure metrics are not biased [16].
ROC-AUC Analysis	Evaluates the model's performance across all possible classification thresholds, providing a comprehensive view of the trade-off between Sensitivity and Specificity.	Reported as a key metric (e.g., AUC of 0.974) to demonstrate the high discriminative power of the skin cancer ensemble model [13].

Pan-cancer research represents a transformative approach in oncology, moving beyond the study of individual cancer types to uncover the shared and unique molecular mechanisms that drive cancer pathogenesis across tissues. This paradigm shift is powered by the integration of multi-omics data—comprehensive molecular profiles spanning the genome, transcriptome, epigenome, and proteome. The systematic collection and analysis of these data types enable researchers to dissect tumor heterogeneity, identify novel biomarkers, and develop more accurate classification models that transcend traditional, histology-based cancer typology [17]. The critical role of data in these endeavors cannot be overstated; the quality, volume, and integration of multi-omics datasets directly dictate the performance and clinical applicability of the computational models built upon them. This guide provides an objective overview of the multi-omics data landscape in pan-cancer research, comparing the performance of various data types and analytical methodologies in the critical task of cancer classification.

Multi-Omics Data Types and Their Roles in Pan-Cancer Analysis

Multi-omics data provides a multi-layered view of the biological processes involved in carcinogenesis. The table below summarizes the key omics data types used in pan-cancer studies, their descriptions, and their primary analytical strengths.

Table 1: Key Multi-Omics Data Types in Pan-Cancer Research

Omics Data Type	Biological Description	Role in Pan-Cancer Analysis
mRNA Expression	Quantifies messenger RNA levels, reflecting gene activity [17].	Identifies dysregulated oncogenes and tumor suppressor genes; used for molecular subtyping and prognostic stratification [17] [18].
miRNA Expression	Measures levels of small non-coding RNAs that regulate gene expression post-transcriptionally [17].	Serves as diagnostic and prognostic biomarkers; helps classify tumor types based on regulatory profiles [17].
lncRNA Expression	Profiles long non-coding RNAs involved in epigenetic and transcriptional regulation [17].	Provides potential diagnostic markers; helps distinguish between tumor types and understand regulatory mechanisms in cancer [17].
Copy Number Variation (CNV)	Identifies gains or losses of genomic DNA segments [17].	Pinpoints genes with amplified oncogenes or deleted tumor suppressors; reveals genomic instability patterns across cancers [17].
DNA Methylation	Maps epigenetic modifications that alter gene expression without changing DNA sequence [19].	Used for epigenetic subtyping; identifies silenced tumor suppressor genes and provides insights into cancer development [20].
Proteomics	Quantifies protein abundance and post-translational modifications [20].	Connects genomic alterations to functional phenotypes; identifies activated pathways and therapeutic targets [20].

Performance Comparison of Omics Data and Models in Classification

The choice of omics data and computational model significantly impacts classification performance. The following tables compare the effectiveness of different approaches based on published studies.

Table 2: Performance of Machine Learning Models on RNA-Seq Data for Cancer Type Classification

This table compares the performance of various machine learning models applied to a pan-cancer RNA-Seq dataset from TCGA, which included 801 samples across five cancer types (BRCA, KIRC, COAD, LUAD, PRAD) [18].

Machine Learning Model	Reported Accuracy (%)	Key Strengths / Context
Support Vector Machine (SVM)	99.87% (5-fold cross-validation) [18]	Achieved the highest accuracy in this comparative study [18].
Random Forest	Performance reported, but accuracy not specified in snippet [18]	Utilized for feature selection and classification; robust to noise [18].
K-Nearest Neighbors (KNN)	Performance reported [18]	Applied in combination with genetic algorithms for feature selection [17].
Decision Tree	Performance reported [18]	Provides interpretable models [18].
Artificial Neural Network (ANN)	Performance reported [18]	A baseline deep learning approach [18].

Table 3: Performance of Data Types and Advanced Models in Pan-Cancer Classification

This table synthesizes findings from multiple studies that utilized different omics data and more complex models, including deep learning, for pan-cancer classification.

Data Type / Model	Reported Performance	Study Context / Key Findings
Convolutional Neural Network (CNN)	95.59% precision in classifying 33 cancers [17]	Leveraged guided Grad-CAM for biomarker identification, adding interpretability [17].
miRNA Expression + Random Forest	92% sensitivity in classifying 32 tumor types [17]	Combined genetic algorithms with Random Forest for feature selection and classification [17].
mRNA Expression + KNN	90% precision in classifying 31 tumor types [17]	Used a genetic algorithm for feature selection prior to classification [17].
Denoising Autoencoder + Multi-Kernel Learning	Superior performance with NMI gains up to 0.78 [21]	Effectively integrated multi-omics data for cancer subtyping in LGG and KIRC [21].
Large Language Models (GPT-4o)	81.9% accuracy on free-text EHR diagnoses [22]	Demonstrated strong performance in categorizing unstructured clinical notes into 14 cancer types [22].
BioBERT	90.8% accuracy on structured ICD codes [22]	A domain-specific model that excelled in processing structured clinical data [22].

Experimental Protocols for Pan-Cancer Classification

To ensure reproducibility and robust model performance, researchers follow standardized experimental workflows. Below is a detailed protocol for a typical pan-cancer classification study using machine learning on omics data.

Data Acquisition and Curation

• Data Source: The Cancer Genome Atlas (TCGA) is the primary source for publicly available, harmonized multi-omics data. Repositories like the UCI Machine Learning Repository and the MLOmics database provide pre-processed, model-ready datasets [18] [19]. For example, the "Gene Expression Cancer RNA-Seq" dataset from UCI contains 801 samples from five cancer types, with expression data for 20,531 genes [18].
• Data Types: Studies may focus on a single omics type (e.g., mRNA) or integrate multiple types (e.g., mRNA, miRNA, methylation, CNV) [17] [19].

Data Preprocessing and Feature Selection

This step is critical for handling the high-dimensionality of omics data, where the number of features (genes) far exceeds the number of samples.

• Normalization: Data is normalized to correct for technical variability. For RNA-Seq data, FPKM values are often log-transformed [19].
• Quality Control: Features with zero expression in more than 10% of samples or with undefined values are removed [19].
• Feature Selection: Dimensionality reduction is performed to identify the most informative genes and prevent overfitting. Common methods include:
  • Lasso Regression (L1 Regularization): Performs variable selection by shrinking some coefficients to exactly zero, leaving a subset of relevant features [18].
  • Ridge Regression (L2 Regularization): Shrinks coefficients to reduce model complexity and multicollinearity without eliminating features entirely [18].
  • ANOVA Testing: Used to identify genes with significant variance across multiple cancer types, selecting the top features for the "Top" feature version in the MLOmics database [19].

Model Training and Validation

• Model Selection: A range of classifiers are evaluated, from traditional ML models (e.g., SVM, Random Forest) to deep learning architectures (e.g., CNN, Denoising Autoencoders) [17] [18].
• Validation Strategy: A rigorous validation protocol is essential for reliable performance estimates:
  • Train-Test Split: The dataset is split, typically with 70% of samples for training and 30% held out for testing [18].
  • K-Fold Cross-Validation: The data is partitioned into k folds (e.g., k=5); the model is trained on k-1 folds and validated on the remaining fold, repeated until each fold has been used for validation. This provides a more robust performance measure [18].
• Evaluation Metrics: Models are evaluated using metrics such as:
  • Accuracy: The proportion of correct predictions among the total predictions.
  • Precision, Recall, and F1-Score: Metrics that provide a more nuanced view of performance, especially for imbalanced datasets [19].
  • Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI): Used to evaluate the quality of clustering in subtyping tasks [19].

The following diagram illustrates the standard workflow for building a pan-cancer classification model.

Figure 1: Standard Pan-Cancer Classification Workflow.

Integrated Multi-Omics Analysis: A Pathway to Deeper Insights

While single-omics analyses are powerful, integrating multiple data types provides a more holistic view of cancer biology. Advanced computational frameworks are required to fuse these disparate data layers effectively. The DAE-MKL (Denoising Autoencoder-Based Multi-Kernel Learning) framework is one such method that integrates genomic, transcriptomic, and epigenomic data [21]. Denoising Autoencoders (DAEs) first extract non-linearly transformed features from each omics data type, reducing noise and redundancy. These refined feature representations are then integrated using Multi-Kernel Learning (MKL), which constructs a composite kernel to capture complex relationships across omics layers, ultimately leading to more accurate identification of cancer subtypes [21]. This approach has been validated on real datasets from TCGA, identifying subtypes of low-grade glioma and kidney renal clear cell carcinoma with significant survival differences [21].

The following diagram illustrates this integrative architecture.

Figure 2: Multi-Omics Integration with DAE-MKL.

Successful pan-cancer research relies on a suite of publicly available data resources, computational tools, and software libraries. The table below details key components of the modern pan-cancer research toolkit.

Table 4: Essential Resources for Pan-Cancer Multi-Omics Research

Resource / Tool Name	Type / Category	Primary Function in Research
The Cancer Genome Atlas (TCGA)	Data Repository	The cornerstone resource providing comprehensive, multi-omics data for over 10,000 tumor samples across 33 cancer types [17] [20].
MLOmics	Processed Database	An open, unified database providing off-the-shelf, preprocessed multi-omics datasets (mRNA, miRNA, methylation, CNV) for machine learning, including "Original," "Aligned," and "Top" feature versions [19].
UCSC Genome Browser	Data Portal & Visualization	An interactive platform that integrates various types of molecular data (e.g., CNV, methylation, gene expression) and supports efficient data analysis and visualization [17].
BioBERT	Computational Model	A domain-specific language model pre-trained on biomedical literature, fine-tuned for tasks like classifying cancer diagnoses from clinical text in EHRs [22].
Cbioportal	Analysis Portal	A web resource for exploring, visualizing, and analyzing multi-dimensional cancer genomics data from TCGA and other studies, including mutation pattern analysis [23].
Python (scikit-learn, PyTorch, TensorFlow)	Programming Software	The primary programming environment for implementing data preprocessing, feature selection, machine learning, and deep learning models [18].
R (survival, limma, maftools)	Statistical Software	Widely used for statistical analysis, differential expression, survival analysis, and genomic data visualization [20] [23].

The landscape of pan-cancer research is unequivocally data-driven. The performance of cancer classification and subtyping models is directly contingent on the richness of the underlying multi-omics data and the sophistication of the methods used to integrate and analyze it. As this guide has illustrated, while models like SVMs can achieve remarkably high accuracy on single-omics data, the future of precision oncology lies in the seamless integration of diverse molecular data types—from genomics and transcriptomics to proteomics and epigenomics. Frameworks like DAE-MKL that effectively reduce noise and leverage complementary information from multiple omics layers are demonstrating superior performance in identifying clinically relevant cancer subtypes. For researchers and drug developers, the path forward involves leveraging centralized, model-ready resources like MLOmics, adhering to rigorous experimental and validation protocols, and continuously adopting advanced integrative analytical methods. This disciplined, data-centric approach is critical for translating the vast potential of pan-cancer studies into tangible improvements in cancer diagnosis, prognosis, and treatment.

While traditional metrics like accuracy, precision, and recall provide valuable isolated insights into model performance, their individual limitations are particularly pronounced in cancer classification research. The introduction of composite scores, primarily the F1 score, represents a critical advancement for evaluating models where class imbalance is common and both false positives and false negatives carry significant clinical consequences [14] [24]. This guide objectively compares the performance of models using single metrics against those evaluated with the F1 score, providing researchers and drug development professionals with experimental data and methodologies to inform their model validation protocols.

The Critical Limitation of Single Metrics in Cancer Diagnostics

In cancer classification, datasets are often inherently imbalanced, with rare cancer types or positive disease cases vastly outnumbered by normal samples or more common cancers [14]. In such contexts, relying solely on accuracy can be profoundly misleading.

• The Accuracy Deception: A model that simply always predicts "no cancer" on a dataset where 95% of samples are truly negative will achieve 95% accuracy, yet fail completely at its primary task of identifying cancer cases [25]. This metric measures overall correctness but fails when the cost of errors is unevenly distributed [24].
• Precision and Recall Trade-Offs: Precision (the measure of correctness among positive predictions) and Recall (the measure of ability to find all positive instances) often exist in tension [14].
  • High Precision, Low Recall: A model is cautious, generating few false positives but missing many true positive cases (e.g., failing to identify actual cancers).
  • High Recall, Low Precision: A model is aggressive, catching most positive cases but also generating many false alarms (e.g., over-diagnosing healthy patients as having cancer) [25].
• The Composite Solution: The F1 score, calculated as the harmonic mean of precision and recall, balances this trade-off, providing a single metric that only achieves a high value when both precision and recall are high [14] [25]. The formula is: F1 Score = 2 * (Precision * Recall) / (Precision + Recall) [14].

Quantitative Performance Comparison: Single Metrics vs. F1 Score

The following tables summarize experimental data from recent cancer classification studies, demonstrating model performance across single metrics and the composite F1 score.

Table 1: Performance Metrics of Recent Multi-Cancer Classification Deep Learning Models

Model / Framework	Cancer Types	Accuracy	Precision	Recall	F1 Score	Reference / Dataset
GraphVar (Multi-representation DL)	33 types from TCGA	99.82%	99.85%	99.82%	99.82%	[26]
CancerDet-Net (Vision Transformer)	9 subtypes across 4 types (Lung, Colon, Skin, Breast)	98.51%	Data Not Specified	Data Not Specified	>98.00%*	LC25000, ISIC 2019, BreakHis [27]
CNN-RF / CNN-LR (Hybrid Model)	Skin Cancer	99.00%	Data Not Specified	Data Not Specified	>98.00%*	HAM10000 [28]

Note: For studies reporting accuracy >98%, it is inferred that the F1 score is similarly high, as major discrepancies between metrics would typically be noted.

Table 2: Comparative Performance in a Binary Classification Scenario with Class Imbalance

Evaluation Metric	Model A (High Accuracy)	Model B (High F1 Score)
Description	Naive model that predominantly predicts the majority class.	Balanced model optimized for the F1 score.
Accuracy	95.0%	90.0%
Precision	50.0%	85.0%
Recall	10.0%	80.0%
F1 Score	16.7%	82.4%

Table 2 illustrates a hypothetical scenario common in cancer screening. While Model A appears superior in accuracy, its low F1 score reveals poor effectiveness at identifying the positive class. Model B, with a high F1 score, is clinically more useful. [24] [25]

Experimental Protocols and Methodologies

To ensure the validity and reproducibility of composite score evaluations, researchers should adhere to rigorous experimental protocols. The following methodologies are drawn from cited studies.

Data Preparation and Model Training for Multi-Cancer Classification

The GraphVar framework provides a robust protocol for developing a classifier evaluated with high F1 scores [26]:

• Data Sourcing: Somatic variant data (e.g., Mutation Annotation Format files) are retrieved from repositories like The Cancer Genome Atlas (TCGA).
• Cohort Curation: A rigorous multi-step pipeline removes duplicate patient entries and verifies each sample corresponds to a unique patient to prevent data leakage.
• Data Partitioning: The dataset is partitioned at the patient level into three mutually exclusive sets:
  • Training Set (70%): For model training.
  • Validation Set (10%): For hyperparameter optimization and early stopping.
  • Independent Test Set (20%): For the final, unbiased evaluation of the fully trained model.
  • Stratified sampling is employed to preserve the proportional representation of all cancer types in each partition.
• Multi-Representation Learning:
  • Variant Map Construction: Gene-level variants are encoded into spatial variant maps (images), with color channels denoting different mutation types (e.g., SNPs, insertions, deletions).
  • Numeric Feature Matrix: A separate matrix captures quantitative properties like allele frequencies and mutation spectra.
• Model Architecture & Training:
  • A dual-stream deep neural network is used. A ResNet-18 backbone extracts features from variant images, while a Transformer encoder models the numeric feature matrix.
  • Features from both streams are concatenated into a comprehensive feature vector and passed to a classification head.

GraphVar Experimental Workflow: The process from data sourcing to model evaluation, highlighting the independent test set for unbiased F1 score calculation. [26]

The Double-Loop Cross-Validation Protocol

For robust feature selection and classifier evaluation without a single held-out test set, the Amsterdam Classification Evaluation Suite (ACES) implements a Double-Loop Cross-Validation (DLCV) protocol [29].

• Outer Loop (Performance Estimation):
  • The dataset (D) is split into five parts.
  • Iteratively, four parts serve as the training set, and one part is the test set.
• Inner Loop (Model Selection):
  • The training set from the outer loop is again split into five parts.
  • This inner set is used to train models with different numbers of features and select the optimal model configuration.
• Feature Selection & Classification:
  • Within the inner loop, feature selection methods (e.g., single-gene or composite-feature methods) provide a ranked list of features.
  • Classifiers (e.g., Nearest Mean Classifiers) are trained by sequentially adding features according to their ranking.
  • The model performance from the inner loop guides the selection of the best model to be evaluated on the outer loop's test set.
• Performance Aggregation: The process is repeated for all outer and inner folds, and the results are aggregated to produce a final, stable performance estimate, including the F1 score.

Double-Loop Cross-Validation: This protocol ensures strict separation between training and testing data for reliable F1 score estimation. [29]

The Scientist's Toolkit: Essential Research Reagents & Materials

The following table details key solutions and materials essential for conducting rigorous cancer classification research with composite metric evaluation.

Table 3: Essential Research Reagents and Computational Tools for Cancer Classification

Item / Solution	Function / Application	Specific Use-Case Example
The Cancer Genome Atlas (TCGA)	A comprehensive public repository of genomic, epigenomic, and clinical data from over 20,000 primary cancers across 33 cancer types.	Sourcing somatic variant data (MAF files) for training and testing multi-cancer classification models like GraphVar [26].
Spatial Transcriptomics (ST) Slides	Glass slides with oligonucleotides to capture mRNAs from histological tissue sections while maintaining spatial information.	Generating spatially-resolved gene expression data for breast cancer region classification (DCIS vs. IDC) in machine learning models [30].
Amsterdam Classification Evaluation Suite (ACES)	A Python package for objective evaluation of classification and feature-selection methods, including DLCV protocols.	Standardized performance comparison of single-gene classifiers versus composite-feature classifiers on large, pooled breast cancer gene expression datasets [29].
PRO-CTCAE (Patient-Reported Outcomes)	A library of items designed for patient-reported adverse event monitoring in oncology clinical trials.	Developing composite grading algorithms to map multiple symptom attributes (frequency, severity, interference) into a single, clinically actionable toxicity grade [31].
PyTorch / scikit-learn	Open-source software libraries for deep learning (PyTorch) and traditional machine learning (scikit-learn).	Implementing deep learning frameworks (e.g., GraphVar) [26] and support vector machine classifiers for spatial transcriptomics data [30].

The move beyond single metrics to composite scores like the F1 score is not merely a technical adjustment but a fundamental necessity for advancing cancer classification research. As evidenced by state-of-the-art models, the F1 score provides a balanced and stringent assessment that aligns with clinical needs, especially when dealing with imbalanced datasets where both false positives and false negatives are critical. By adopting rigorous experimental protocols, such as those demonstrated by GraphVar and ACES, and leveraging essential research tools, scientists and drug developers can ensure their models are robust, reliable, and truly fit for the purpose of improving cancer diagnostics and patient outcomes.

From Theory to Practice: Applying Metrics in Complex Cancer Research Scenarios

In the field of oncology research, accurate evaluation of classification models is not merely a statistical exercise—it can directly impact clinical decision-making and patient outcomes. Machine learning models for cancer classification must reliably distinguish between multiple cancer types, disease stages, or molecular subtypes, often working with imbalanced datasets where some categories are naturally rare yet clinically significant. Macro and micro averaging provide two distinct philosophical approaches to summarizing model performance across multiple classes, each with different implications for how we prioritize certain types of classification errors in medical applications [32].

The choice between macro and micro averaging becomes particularly crucial in cancer informatics, where the clinical cost of misclassifying a rare but aggressive cancer type may far outweigh the cost of misclassifying more common variants. Understanding these metrics enables researchers to select evaluation frameworks that align with clinical priorities, ensuring that models are optimized for patient benefit rather than merely abstract statistical performance [16].

Conceptual Foundations: Macro vs. Micro Averaging

Core Definitions and Mathematical Formulations

In multi-class classification settings, performance metrics such as precision, recall, and F1-score cannot be directly computed as in binary classification without first establishing an aggregation method. Macro and micro averaging represent two fundamentally different approaches to this challenge [33].

Macro-averaging calculates metrics independently for each class and then computes the arithmetic mean, thereby treating all classes equally regardless of their frequency in the dataset. For a multi-class system with N classes, the macro-averaged precision is calculated as:

[ \text{Macro-P} = \frac{\sum{i=1}^{N} Pi}{N} ]

where ( P_i ) represents the precision for class i [33].

Micro-averaging aggregates the contributions of all classes by summing all true positives, false positives, and false negatives across all classes, then calculating the metrics based on these global sums. The micro-averaged precision is calculated as:

[ \text{Micro-P} = \frac{\sum{i=1}^{N} TPi}{\sum{i=1}^{N} TPi + \sum{i=1}^{N} FPi} ]

where ( TPi ) and ( FPi ) represent true positives and false positives for class i, respectively [34] [35].

Visualizing the Calculation Pathways

The fundamental difference between macro and micro averaging lies in the sequence of aggregation operations applied to the per-class confusion matrices. The following diagram illustrates these distinct calculation pathways:

Key Behavioral Differences in Imbalanced Scenarios

The conceptual differences between macro and micro averaging lead to dramatically different behaviors when dealing with imbalanced datasets, which are common in medical applications [34].

Consider a hypothetical cancer classification system with four classes:

• Class A: 1 TP and 1 FP → Precision = 0.5
• Class B: 10 TP and 90 FP → Precision = 0.1
• Class C: 1 TP and 1 FP → Precision = 0.5
• Class D: 1 TP and 1 FP → Precision = 0.5

The macro-average precision would be ( (0.5 + 0.1 + 0.5 + 0.5) / 4 = 0.4 ), while the micro-average precision would be ( (1 + 10 + 1 + 1) / (2 + 100 + 2 + 2) = 13/106 ≈ 0.123 ) [34].

This example demonstrates how macro-averaging can present a more optimistic view by giving equal weight to each class's performance, while micro-averaging provides a more pessimistic but data-volume-weighted perspective that strongly reflects performance on the majority class [34].

Experimental Evidence from Cancer Classification Research

Multiomics Cancer Type Classification

A 2025 study on multiomics cancer classification provides compelling real-world evidence of how these averaging techniques perform in practice. Researchers developed a stacked deep learning ensemble to classify five common cancer types in Saudi Arabia: breast, colorectal, thyroid, non-Hodgkin lymphoma, and corpus uteri [8]. The model integrated RNA sequencing, somatic mutation, and DNA methylation profiles using a stacking ensemble of five established methods: support vector machine, k-nearest neighbors, artificial neural network, convolutional neural network, and random forest [8].

Table 1: Performance Metrics for Multiomics Cancer Classification

Data Type	Accuracy	Note on Averaging
Multiomics (Integrated)	98%	Demonstrates micro-like behavior
RNA Sequencing	96%	Majority class influence
Methylation	96%	Majority class influence
Somatic Mutation	81%	Lower performance on sparse data

The high accuracy (98%) with multiomics data integration essentially reflects a micro-averaged perspective, as it gives equal weight to each instance rather than each class [8]. The dataset exhibited notable class imbalance, with breast cancer (BRCA) having 1,223 cases while non-Hodgkin lymphoma (NHL) had only 481 cases in the RNA sequencing data [8]. Despite this imbalance, the overall accuracy remained high, suggesting the model performed well on the majority classes.

Cancer Diagnosis Categorization in Electronic Health Records

Another 2025 study evaluated large language models and BioBERT for classifying cancer diagnoses from both structured ICD codes and unstructured free-text entries in electronic health records [32]. This research specifically utilized weighted macro F1-scores, recognizing the importance of accounting for class imbalance in clinical applications.

Table 2: Performance on Cancer Diagnosis Categorization

Model	Data Format	Weighted Macro F1-Score	Accuracy
BioBERT	ICD Codes	84.2	90.8%
GPT-4o	ICD Codes	~84.0	90.8%
GPT-4o	Free-text	71.8	81.9%
BioBERT	Free-text	61.5	81.6%

The researchers explicitly chose weighted macro F1-score as a primary metric because it "balances precision and recall across all diagnosis categories while assigning greater influence to frequently occurring diagnoses via sample weights" [32]. This approach ensured that performance on common categories meaningfully impacted the overall score while still considering the model's ability to classify less frequent diagnoses—an essential consideration for clinical deployment where even rare cancers must be identified correctly.

Calculation Methodologies and Workflows

Practical Computation of Averaging Metrics

The computational workflow for deriving macro and micro averages follows systematic processes that can be visualized as parallel pathways. The following diagram details the specific calculation steps for each approach:

Important Mathematical Properties

A critical mathematical relationship emerges in micro-averaging: for multi-class classification where each instance receives a single label, the micro-averaged precision, micro-averaged recall, micro F1-score, and overall accuracy are all numerically identical [35]. This occurs because:

[ \text{Micro-P} = \frac{\sum TP}{\sum TP + \sum FP} = \frac{\sum TP}{\sum TP + \sum FN} = \text{Micro-R} ]

when each data point is assigned to exactly one class, and:

[ \text{Accuracy} = \frac{\sum TP}{\text{Total Instances}} = \frac{\sum TP}{\sum TP + \sum FP} = \text{Micro-P} ]

since in single-label classification, every false positive for one class is necessarily a false negative for another class, making (\sum FP = \sum FN) [35].

Guidelines for Metric Selection in Cancer Research

Decision Framework for Researchers

Selecting between macro and micro averaging depends primarily on the clinical context, class distribution characteristics, and the relative importance of minority classes in the specific research application. The following decision framework provides guidance for researchers:

Table 3: Metric Selection Guide for Cancer Classification Research

Scenario	Recommended Metric	Rationale	Clinical Example
Balanced classes with equal clinical importance	Macro-average	Treats all cancer types equally	Classifying common cancers with similar prevalence
Imbalanced data with majority classes dominating	Micro-average	Reflects performance on most frequent cases	Screening where common cancers represent most cases
Imbalanced data with critical minority classes	Weighted Macro-average	Balances recognition of rare but lethal cancers	Identifying rare pediatric cancers with high mortality
Need for intuitive, overall performance measure	Micro-average (same as accuracy)	Easily interpretable for clinical stakeholders	Communicating model performance to hospital administrators
Focus on specific rare cancer detection	Per-class metrics + Macro-average	Ensures minority class performance is visible	Early detection of rare but aggressive cancer subtypes

Special Considerations for Medical Applications

In cancer research, the choice of evaluation metric should align with clinical priorities. If all cancer types are considered equally important regardless of prevalence, macro-averaging provides a more appropriate evaluation framework [34]. However, if the clinical application will predominantly encounter majority classes, micro-averaging may better reflect real-world performance [35].

For datasets with significant class imbalance where all classes remain clinically important, the weighted macro-average offers a pragmatic compromise. This approach calculates the macro-average but weights each class's contribution according to its support (the number of true instances), thus providing a balance between the macro and micro perspectives [36].

Essential Research Reagent Solutions

The experimental protocols cited in this guide utilize several key computational tools and frameworks that constitute essential research reagents for conducting similar investigations in cancer classification research.

Table 4: Essential Research Reagents for Cancer Classification Metrics Research

Reagent/Solution	Function	Example Implementation
Python Machine Learning Stack	Core computational environment	Scikit-learn for metric calculation
Statistical Bootstrap Methods	Uncertainty quantification for metrics	95% confidence intervals for F1-scores
Multiomics Data Integration Platforms	Handling diverse biological data types	TCGA and LinkedOmics dataset access
Deep Learning Frameworks	Implementing complex classification models	TensorFlow, PyTorch for neural networks
Natural Language Processing Tools	Processing clinical text data	BioBERT for biomedical text classification
Ensemble Learning Methodologies	Combining multiple classification approaches	Stacking SVMs, KNN, ANN, CNN, and Random Forest

Macro and micro averaging provide complementary perspectives on model performance in multi-class cancer classification tasks. The experimental evidence from recent cancer informatics research demonstrates that metric selection should be driven by clinical requirements rather than statistical convenience. While micro-averaging offers an intuitive volume-weighted perspective that often aligns with overall accuracy, macro-averaging ensures that rare cancer types receive appropriate consideration in model evaluation. Weighted macro-averaging represents a particularly valuable approach for imbalanced medical datasets where all classes hold clinical significance. As cancer classification models continue to evolve in complexity and clinical application, thoughtful metric selection will remain essential for ensuring that these tools deliver meaningful improvements in patient care and oncological outcomes.

In the high-stakes domain of cancer classification, the pursuit of model performance often leads researchers to a deceptive benchmark: accuracy. This metric, defined as the proportion of correct predictions among all classifications, becomes particularly misleading when dealing with imbalanced medical datasets where healthy patients significantly outnumber those with disease [37] [5]. Consider a model designed to detect a cancer type present in only 5% of a population. A naive classifier that simply predicts "no cancer" for every case would achieve 95% accuracy, creating the illusion of competence while failing completely at its intended purpose [37]. This phenomenon, known as the accuracy paradox, underscores a critical limitation in traditional evaluation approaches for medical machine learning applications.

The challenge of imbalanced data is particularly pronounced in cancer diagnostics and prognosis, where the number of diseased patients is naturally smaller than healthy individuals [38] [39]. Standard machine learning algorithms, designed with the assumption of relatively balanced class distributions, frequently develop a bias toward the majority class, effectively ignoring the rare cases that are often of greatest clinical interest [39]. The consequences of such oversights can be dire—false negatives in cancer detection may delay critical treatments, adversely affecting patient outcomes and survival rates [39]. Consequently, researchers must look beyond accuracy to metrics that more accurately reflect model performance on imbalanced datasets, particularly those that prioritize correct identification of the minority class.

Beyond Accuracy: Essential Evaluation Metrics for Imbalanced Data

When evaluating cancer classification models on imbalanced datasets, researchers should consider multiple metrics that collectively provide a more nuanced understanding of model performance. The following table summarizes key evaluation metrics and their relevance to imbalanced cancer classification problems:

Metric	Formula	Clinical Interpretation	When to Prioritize
Precision	( \frac{TP}{TP + FP} )	When the model predicts cancer, how often is it correct?	When false positives (unnecessary biopsies) are clinically concerning [5] [40]
Recall (Sensitivity)	( \frac{TP}{TP + FN} )	What proportion of actual cancer cases were detected?	When false negatives (missed cancers) are dangerous [5] [39]
F1 Score	( 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} )	Harmonic mean balancing precision and recall	When seeking a single metric that balances both false positives and false negatives [37] [40]
ROC AUC	Area under ROC curve	Model's ability to distinguish cancerous from non-cancerous cases across thresholds	When overall ranking performance is important and dataset isn't severely imbalanced [40] [41]
PR AUC	Area under Precision-Recall curve	Model performance focused specifically on the positive (cancer) class	Preferred over ROC AUC for imbalanced data [41]

For cancer classification, the choice of metric should reflect clinical priorities. Recall becomes paramount when missing a cancer case (false negative) could have severe consequences, as early detection significantly improves outcomes [5]. Precision gains importance when false positives lead to invasive follow-up procedures that carry their own risks and costs [40]. The F1 score provides a balanced perspective when both types of errors need consideration.

The ROC AUC (Receiver Operating Characteristic Area Under Curve) represents the probability that a randomly chosen positive instance (cancer case) is ranked higher than a randomly chosen negative instance (non-cancer case) [41]. However, with imbalanced medical data, the Precision-Recall AUC (PR AUC) often provides a more informative assessment as it focuses specifically on the model's performance regarding the positive class, without being influenced by the abundance of true negatives [41].

Experimental Insights: Performance Comparisons in Cancer Classification

Recent research provides compelling evidence for the necessity of alternative metrics and techniques when working with imbalanced cancer datasets. A comprehensive 2024 study evaluated 19 resampling methods and 10 classifiers across five cancer datasets, revealing significant performance differences between methods [38].

Table: Classifier Performance on Imbalanced Cancer Data (Adapted from [38])

Classifier	Mean Performance (%)	Key Strengths	Optimal Resampling Partner
Random Forest	94.69%	Robustness, handles high-dimensional data	SMOTEENN
Balanced Random Forest	94.69%	Built-in handling of class imbalance	(Native implementation)
XGBoost	94.69%	Handling complex non-linear relationships	SMOTEENN
Baseline (No Resampling)	91.33%	-	-

Table: Resampling Method Performance on Cancer Data (Adapted from [38])

Resampling Method	Mean Performance (%)	Category	Key Characteristics
SMOTEENN	98.19%	Hybrid	Combines oversampling and cleaning
IHT	97.20%	Under-sampling	Removes noisy majority class instances
RENN	96.48%	Under-sampling	Removes instances misclassified by k-NN

The experimental protocol employed in this research involved systematic comparison across multiple diagnostic and prognostic cancer datasets, including the Wisconsin Breast Cancer Database, Lung Cancer Detection Dataset, and SEER Breast Cancer Dataset [38]. Researchers applied resampling techniques from three categories (oversampling, undersampling, and hybrid methods) before training and evaluating classifiers using appropriate metrics for imbalanced data. The performance advantage of hybrid sampling methods like SMOTEENN highlights the effectiveness of combining synthetic minority oversampling with cleaning of the majority class.

In a separate study focused on osteosarcoma classification, researchers found that combining random oversampling with the Extra Trees algorithm achieved 97.8% area under the ROC curve with acceptably low false alarm and misdetection rates [42]. This further reinforces the importance of combining appropriate data-level techniques with well-suited algorithms for optimal performance on imbalanced medical data.

Methodological Approaches: Techniques for Handling Imbalanced Data

Data-Level Techniques: Resampling Methods

Resampling methods modify the training dataset to create a more balanced distribution between classes, enabling standard algorithms to learn more effectively from minority class examples:

• Oversampling: Increasing the representation of the minority class by creating copies of existing instances or generating synthetic examples [37] [39]. The Synthetic Minority Oversampling Technique (SMOTE) creates synthetic samples by interpolating between existing minority class instances, though it may not preserve non-linear relationships in the data [37].

• Undersampling: Reducing the majority class instances by randomly removing examples or employing more sophisticated selection methods [37] [39]. Techniques like RENN (Repeated Edited Nearest Neighbors) remove majority class instances that are misclassified by k-nearest neighbors, effectively cleaning the decision boundary [38].

• Hybrid Methods: Combining both oversampling and undersampling approaches for improved effectiveness [38] [39]. SMOTEENN, the top-performing method in recent cancer research, first applies SMOTE to generate synthetic minority instances, then uses ENN (Edited Nearest Neighbors) to remove both majority and minority instances identified as noisy [38].

Algorithm-Level Techniques and Threshold Tuning

Beyond manipulating training data, several algorithm-level approaches can enhance performance on imbalanced cancer datasets:

• Cost-Sensitive Learning: Modifying algorithms to impose heavier penalties for misclassifying minority class instances [37]. This approach aligns with clinical reality where the cost of missing a cancer case typically far exceeds the cost of a false alarm.

• Ensemble Methods: Combining multiple models to improve overall performance and robustness [38] [42]. Random Forest and Balanced Random Forest have demonstrated particular effectiveness on imbalanced cancer data, as evidenced by their top performance in comparative studies [38].

• Threshold Tuning: Adjusting the default classification threshold (typically 0.5) to optimize for specific metrics [37]. Increasing the threshold makes the model more conservative in predicting cancer, potentially improving precision, while decreasing the threshold makes it more sensitive, potentially improving recall. This approach allows clinicians to calibrate models based on specific clinical requirements and risk tolerance.

Diagram: Comprehensive Approach to Handling Imbalanced Cancer Data

Successfully navigating the challenges of imbalanced cancer datasets requires access to appropriate datasets, algorithms, and evaluation frameworks. The following table outlines key resources for researchers working in this domain:

Table: Research Reagent Solutions for Imbalanced Cancer Classification

Resource Category	Specific Tools & Datasets	Function & Application	Access Information
Public Cancer Datasets	Wisconsin Breast Cancer DB [38]	Binary classification (benign/malignant); 699 samples	Publicly available via Kaggle
	Lung Cancer Detection Dataset [38]	Risk assessment with demographic/clinical factors; 309 samples	Publicly available via Kaggle
	SEER Breast Cancer Dataset [38]	Prognostic modeling with clinical outcomes; 4024 patients	Publicly available via Kaggle
Resampling Algorithms	SMOTE [38] [39]	Synthetic minority oversampling to balance class distribution	Implemented in imbalanced-learn (Python)
	SMOTEENN [38]	Hybrid approach combining oversampling and cleaning	Implemented in imbalanced-learn (Python)
	RENN, IHT [38]	Undersampling methods that remove noisy majority instances	Implemented in imbalanced-learn (Python)
Classification Algorithms	Random Forest [38]	Ensemble method demonstrating top performance on cancer data	scikit-learn, Python
	Balanced Random Forest [38]	Random Forest variant with built-in class weight adjustment	scikit-learn, Python
	XGBoost [38]	Gradient boosting effective with complex non-linear relationships	XGBoost library, Python
Evaluation Metrics	PR AUC [41]	Focused assessment of positive class performance	scikit-learn, Python
	F1 Score [37] [40]	Balanced measure of precision and recall	scikit-learn, Python
	Recall/Sensitivity [5] [39]	Critical for minimizing false negatives in cancer detection	scikit-learn, Python

The critical evaluation of model performance on imbalanced cancer datasets demands a nuanced approach that moves beyond traditional accuracy metrics. As demonstrated by comparative studies, employing appropriate evaluation metrics—particularly recall, F1 score, and PR AUC—provides a more clinically relevant assessment of model capability [38] [41]. Furthermore, combining strategic resampling techniques like SMOTEENN with robust classifiers such as Random Forest delivers substantially improved performance on minority class prediction without sacrificing overall model quality [38].

Future research directions in this domain include developing more sophisticated hybrid approaches that integrate data-level and algorithm-level solutions [39], creating domain-specific evaluation metrics that incorporate clinical costs and benefits, and advancing interpretability methods that build trust in model predictions among healthcare professionals [43]. As machine learning continues to transform cancer diagnostics and prognosis, maintaining rigorous, clinically-informed evaluation standards will be essential for deploying models that genuinely enhance patient care and outcomes.

In the development of cancer classification models, from early detection to prognosis prediction, evaluating model performance is as crucial as the algorithm design itself. The Receiver Operating Characteristic (ROC) curve and the Area Under this Curve (AUC) provide a comprehensive framework for assessing diagnostic accuracy across all possible decision thresholds [44] [45]. This is particularly vital in clinical settings, where the consequences of false negatives (missed cancers) and false positives (unnecessary biopsies) must be carefully balanced based on the specific clinical context [44] [46].

The ROC curve visually represents the trade-off between a model's sensitivity (ability to correctly identify cancer cases) and its 1-specificity (tendency to falsely classify healthy cases as cancer) at every possible classification threshold [44] [45]. The AUC summarizes this curve into a single numeric value representing the model's overall ability to distinguish between positive (cancer) and negative (non-cancer) classes [47] [48]. For cancer researchers, this provides an essential tool for selecting optimal models and classification thresholds suited to specific clinical requirements, whether for highly sensitive cancer screening or highly specific confirmatory testing [44].

Core Concepts and Clinical Interpretation

The Anatomy of a ROC Curve

The ROC curve is created by plotting the True Positive Rate (TPR), also known as sensitivity or recall, against the False Positive Rate (FPR), which equals 1-specificity [44] [45]. Each point on the curve represents a sensitivity/specificity pair corresponding to a particular decision threshold [44] [48].

• True Positive Rate (Sensitivity): The proportion of actual cancer cases correctly identified by the model [45]. Calculated as TP/(TP+FN), where TP=True Positives and FN=False Negatives [49].
• False Positive Rate (1-Specificity): The proportion of actual non-cancer cases incorrectly flagged as cancer [45]. Calculated as FP/(FP+TN), where FP=False Positives and TN=True Negatives [49].

The curve's shape reveals critical information about model performance. A curve arching toward the upper-left corner indicates strong discriminatory power, while a curve following the diagonal suggests performance no better than random guessing [44] [45].

Understanding the AUC Metric

The Area Under the ROC Curve (AUC) quantifies the overall performance across all thresholds [44] [47]. The AUC value ranges from 0 to 1 and has a probabilistic interpretation: it represents the probability that the model will rank a randomly chosen positive instance (e.g., a cancer case) higher than a randomly chosen negative instance (e.g., a non-cancer case) [44] [48].

The following diagram illustrates key ROC curve shapes and their corresponding AUC values:

Clinical Meaning of AUC in Cancer Diagnostics

In clinical oncology, the AUC represents an "optimistic" estimate of global diagnostic accuracy when diseased and non-diseased groups are balanced [46]. Research has demonstrated that the AUC provides an upward-biased measure of the proportion of correct classifications at an optimal accuracy cut-off, with the magnitude of bias depending on the shape of the ROC curve [46]. This understanding is essential when translating model performance metrics to expected real-world clinical performance.

For cancer detection, an AUC of 0.8 means there is an 80% probability that the model will correctly rank a random cancer case higher than a random non-cancer case [44] [48]. As a general guideline in medical diagnostics, AUC values of 0.9-1.0 are considered excellent, 0.8-0.9 good, 0.7-0.8 fair, and 0.5-0.7 poor [48].

Experimental Applications in Cancer Research

Case Study: Breast MRI Protocol Comparison

A 2025 study directly compared the diagnostic accuracy of abbreviated versus full MRI protocols for detecting breast lobular carcinoma using ROC analysis [50] [51]. This research exemplifies the application of ROC/AUC methodology in clinical cancer imaging research.

Table 1: Diagnostic Performance of MRI Protocols for Breast Lobular Carcinoma Detection

Protocol	AUC	Sensitivity	Specificity	Clinical Implications
Full MRI Protocol	1.0	100%	100%	Gold standard performance
Abbreviated Protocol (Radiologist A)	0.920	100%	73.3%	High sensitivity, reduced specificity
Abbreviated Protocol (Radiologist B)	0.922	100%	53.5%	Maintained sensitivity, significantly reduced specificity

The study demonstrated that while the abbreviated protocol maintained perfect sensitivity (critical for cancer screening), it showed significantly reduced specificity compared to the full protocol [50]. This trade-off has direct clinical implications: higher false positive rates may lead to unnecessary biopsies and patient anxiety, despite the protocol's advantage of being faster and more cost-effective [50] [51].

Implementation in Microbiome-Cancer Research

Machine learning approaches using microbiome data for cancer characterization represent another significant application of ROC/AUC analysis [52]. Studies have explored using microbial abundance profiles as features for classifiers to distinguish cancer patients from healthy controls [52].

The experimental workflow typically involves:

In this domain, Random Forests and Logistic Regression have shown promising results, though model generalizability remains challenging due to dataset limitations and technical artifacts in microbiome data [52]. ROC analysis provides the standard framework for evaluating and comparing these models across all classification thresholds.

Comparative Analysis of Classification Models

Performance Across Algorithm Types

Different machine learning algorithms produce distinct ROC characteristics when applied to cancer classification tasks. The following table summarizes typical performance patterns:

Table 2: Comparative Performance of Machine Learning Models in Cancer Classification

Model Type	Typical AUC Range	Strengths	Limitations	Common Cancer Applications
Logistic Regression	0.75-0.90	Interpretable, stable, fast training	Limited complex pattern detection	Preliminary screening models
Random Forest	0.80-0.95	Handles high dimensionality, robust to outliers	Black box, can overfit	Microbiome-based classification [52]
Deep Learning	0.85-0.98	Automatic feature extraction, high accuracy	Large data requirements, computationally intensive	Medical imaging analysis
Support Vector Machines	0.78-0.92	Effective in high-dimensional spaces	Sensitive to parameter tuning	Genomic data classification

Practical Implementation Framework

Implementing ROC analysis requires specific computational tools and methodologies. The following code framework illustrates a typical implementation for comparing multiple models:

Research Reagent Solutions for Cancer Classification Studies

Table 3: Essential Research Materials for Microbiome-Based Cancer Classification Studies

Reagent/Resource	Function	Application in Cancer Research
DNA/RNA Extraction Kits	Nucleic acid isolation from samples	Obtain genetic material from tissue, fecal, or blood samples [52]
16S rRNA Sequencing Reagents	Taxonomic profiling of bacteria	Characterize microbiome composition in cancer vs normal samples [52]
Shotgun Metagenomics Kits	Comprehensive genomic analysis	Identify functional potential of cancer-associated microbiomes [52]
The Cancer Genome Atlas (TCGA) Data	Reference genomic datasets	Benchmarking and validation of classification models [52]
Computational Frameworks (scikit-learn, TensorFlow)	Model implementation and evaluation	Develop and validate cancer classification algorithms [47]

Advanced Considerations and Limitations

Threshold Selection for Clinical Deployment

Choosing the optimal operating point on the ROC curve represents a critical decision in clinical implementation [44]. The choice depends on the relative clinical consequences of false positives versus false negatives:

• Conservative Threshold (Low FPR): Appropriate when false positives have serious consequences (e.g., unnecessary invasive biopsies, patient trauma) [44]
• Sensitive Threshold (High TPR): Essential when missing true positives is dangerous (e.g., cancer screening where early detection is critical) [44]
• Balanced Threshold: Suitable when costs of false positives and false negatives are roughly equivalent [44]

Limitations and Complementary Metrics

While ROC/AUC provides valuable insights, several limitations must be considered:

• Class Imbalance Concerns: ROC curves can present an overly optimistic view when dealing with highly imbalanced datasets common in cancer research (where healthy individuals may far outnumber cancer cases) [44] [49]. In such cases, precision-recall curves may offer more meaningful evaluation [44] [52].

• Clinical Relevance: The AUC represents an aggregate measure across all thresholds, but clinical practice typically operates at a single threshold [46]. Additional metrics such as positive and negative predictive values may be more directly informative for clinical decision-making.

• Shape Considerations: The clinical meaning of AUC depends on the shape of the ROC curve, with different curve shapes potentially having identical AUC values but different clinical implications [46].

ROC curve analysis and AUC quantification provide an essential framework for evaluating cancer classification models across all decision thresholds. These tools enable researchers to make informed decisions about model selection and threshold determination based on specific clinical requirements. The application of these methods spans diverse domains from medical imaging to microbiome-based classification, demonstrating their fundamental importance in oncology research.

As cancer characterization models continue to evolve, ROC/AUC analysis will remain central to validating their performance and ensuring their appropriate implementation in clinical practice. Future directions include addressing class imbalance challenges, improving model generalizability across diverse populations, and developing standardized reporting guidelines for ROC analysis in cancer research.

In the field of cancer classification research, the development of accurate diagnostic and prognostic models is often hampered by a fundamental data challenge: class imbalance. This occurs when one class of outcome—such as malignant cases or treatment responders—is significantly rarer than the other. In medical contexts, this imbalance is not merely a statistical inconvenience but reflects the natural prevalence of conditions, where unhealthy individuals are typically outnumbered by healthy ones [39]. For instance, in critical care settings, outcomes like mortality, clinical deterioration, and acute kidney injury often affect only a minority of patients (<10–20%), creating imbalanced datasets where the event of interest is rare [53].

Traditional performance metrics can be misleading under such conditions. Model accuracy becomes an unreliable indicator of practical utility, as a model that simply predicts the majority class for all cases can achieve high accuracy while failing completely to identify the clinically crucial minority cases [54]. This creates an urgent need for evaluation frameworks that remain informative even when positive cases are scarce. The precision-recall (PR) curve has emerged as a particularly valuable tool for these scenarios, offering a more nuanced and clinically relevant assessment of model performance for imbalanced cancer classification tasks [53] [55].

Theoretical Foundations: ROC-AUC vs. PR-AUC

Core Metrics and Their Clinical Interpretation

To understand the relative strengths of the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, one must first grasp the fundamental metrics that underpin them. The ROC curve plots the True Positive Rate (TPR/Recall/Sensitivity) against the False Positive Rate (FPR) at various classification thresholds [55]. The area under this curve (ROC-AUC) provides a single measure of a model's ability to discriminate between classes, with a value of 1.0 representing perfect discrimination and 0.5 representing a random classifier [55] [56].

In contrast, the PR curve plots Precision (Positive Predictive Value) against Recall (Sensitivity) at different thresholds [53] [55]. The area under the PR curve (PR-AUC) summarizes the trade-off between these two metrics, with a stronger focus on the model's performance regarding the positive class.

Key Metric Definitions:

• Recall (Sensitivity/True Positive Rate): Recall = TP / (TP + FN) - The proportion of actual positive cases that the model correctly identifies [55]. This is crucial in cancer detection, as it represents the model's ability to find all affected patients.
• Precision (Positive Predictive Value): Precision = TP / (TP + FP) - The proportion of positive predictions that are correct [53] [55]. This indicates how often the model is right when it flags a case as positive.
• Specificity: Specificity = TN / (TN + FP) - The proportion of actual negative cases that the model correctly identifies [53].
• False Positive Rate: FPR = 1 - Specificity - The proportion of negative cases incorrectly flagged as positive [55].

The Critical Difference: Specificity vs. Precision in Imbalanced Contexts

The fundamental difference between ROC and PR analysis lies in their treatment of true negative cases. ROC curves incorporate specificity, which depends on true negatives, while PR curves substitute specificity with precision, which is independent of true negatives [53]. This distinction becomes critically important in imbalanced datasets.

In cancer classification, where negative cases (healthy individuals) vastly outnumber positive cases (cancer patients), specificity can become misleadingly high. A model can achieve high specificity simply by labeling most cases as negative, which is easy when negatives are abundant. This robustness in specificity inflates the ROC-AUC, creating an "overly optimistic" estimate of performance [53] [57]. Precision, however, remains challenging to optimize because it requires correctly identifying the rare positive cases among many negatives [53].

Table: Comparison of ROC and PR Curves for Model Evaluation

Feature	ROC Curve	PR Curve
Axes	True Positive Rate (Recall) vs. False Positive Rate	Precision vs. Recall
Perfect Point	(0, 1) - Top-left corner	(1, 1) - Top-right corner
Random Baseline	Diagonal line (AUC = 0.5)	Horizontal line at prevalence rate (AUC = prevalence)
Sensitivity to Class Imbalance	Low	High
Focus	Overall class discrimination	Performance on positive class
Clinical Interpretation	Trade-off between sensitivity and false alarms	Trade-off between prediction accuracy and case identification

Empirical Evidence: PR-AUC Superiority in Cancer Classification

Case Study: Predicting Cerebral Edema in Critical Care

A compelling demonstration of PR-AUC's utility comes from a simulated pediatric critical care study involving 200,000 virtual patients with diabetic ketoacidosis, where the outcome of interest—cerebral edema (CE)—had a prevalence of just 0.7% [53]. Researchers built three prediction models (logistic regression, random forest, and XGBoost) and evaluated them using both ROC-AUC and PR-AUC.

Table: Performance Metrics for Cerebral Edema Prediction Models

Model	ROC-AUC (95% CI)	PR-AUC (95% CI)	Usefulness Ratio (AUPRC/Prevalence)
Logistic Regression	0.953 (0.939–0.964)	0.116 (0.095–0.142)	16.6×
Random Forest	0.874 (0.851–0.897)	0.083 (0.068–0.102)	11.9×
XGBoost	0.947 (0.939–0.964)	0.096 (0.082–0.112)	13.7×

The results revealed a critical insight: while all models exhibited excellent ROC-AUC values (>0.85), their PR-AUC values were substantially lower (0.083–0.116) [53]. This discrepancy highlights how ROC-AUC can provide a deceptively favorable impression of performance for imbalanced problems. The PR-AUC enabled a more clinically meaningful interpretation through the "usefulness ratio" (AUPRC divided by outcome prevalence), which showed the logistic regression model was 16.6 times more useful than a random model [53].

Furthermore, the PR curve revealed operational insights not apparent from the ROC curve. At a sensitivity of 0.85–0.90, the logistic regression and XGBoost models achieved a positive predictive value (precision) 5–10% higher than the random forest model [53]. This granular understanding of the precision-recall tradeoff is essential for clinical deployment, where the "number needed to alert" (NNA = 1/PPV) directly impacts alert fatigue and resource utilization [53].

Evidence Across Multiple Domains

The pattern observed in the cerebral edema study extends to other medical domains. Research on breast cancer diagnosis has shown that class imbalance causes models to be biased toward the majority class (healthy cases), potentially leading to missed cancers [58]. Similarly, in a direct comparison across three datasets with varying imbalance levels, the divergence between ROC-AUC and PR-AUC became more pronounced as imbalance increased [57].

Table: ROC-AUC vs. PR-AUC Across Imbalance Levels

Dataset	Class Ratio	ROC-AUC	PR-AUC	Discrepancy
Pima Indians Diabetes	65:35 (Mild Imbalance)	0.838	0.733	Moderate
Wisconsin Breast Cancer	63:37 (Mild Imbalance)	0.998	0.999	Minimal
Credit Card Fraud	99:1 (High Imbalance)	0.957	0.708	Substantial

For the highly imbalanced credit card fraud dataset (99:1 ratio), the ROC-AUC of 0.957 suggested excellent performance, while the substantially lower PR-AUC of 0.708 revealed the model's limited ability to reliably identify the rare positive cases [57]. This pattern directly translates to cancer classification contexts where positive cases are similarly rare.

Methodological Protocols for PR Curve Analysis

Experimental Workflow for Cancer Classification

The following diagram illustrates the comprehensive experimental workflow for evaluating cancer classification models using PR analysis, particularly suited for imbalanced datasets:

Addressing Class Imbalance: Techniques and Considerations

Before even evaluating models with PR analysis, researchers often employ techniques to mitigate class imbalance during model training. The choice of technique can significantly impact model performance and generalizability.

Table: Class Imbalance Handling Techniques in Medical Imaging

Technique	Mechanism	Advantages	Limitations
Class Weighting	Algorithm-level: Adjusts loss function to weight minority class higher [58]	Simple implementation; No data modification required	May not suffice for extreme imbalance
Oversampling	Data-level: Replicates minority class instances (e.g., SMOTE) [39]	Balances class distribution; Utilizes all majority cases	Risk of overfitting to repeated patterns
Undersampling	Data-level: Removes majority class instances [58] [39]	Reduces computational cost; Balances classes	Potential loss of informative majority samples
Synthetic Data Generation	Data-level: Creates artificial minority samples (GANs, diffusion models) [58] [59]	Can create diverse, realistic samples; Addresses data scarcity	Computational complexity; Quality control challenges

Research comparing these techniques in breast cancer classification found that while all standard methods reduced bias toward the majority class, undersampling could reduce AUC by 0.066 in cases of extreme imbalance (19:1 benign to malignant ratio) [58]. Synthetic lesion generation showed promise, increasing AUC by up to 0.07 on out-of-distribution test sets compared to other methods [58].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table: Key Research Reagents for PR Curve Analysis in Cancer Classification

Reagent/Solution	Function	Example Application
pROC Package (R)	Calculates and visualizes ROC curves [53]	Model discrimination analysis
PRROC Package (R)	Computes PR curves and AUPRC using piecewise trapezoidal integration [53]	Precision-recall analysis for imbalanced data
EfficientNetV2L Architecture	Deep learning backbone for image classification with compound scaling [60]	Skin cancer classification from lesion images
3D ResNet50 Model	Captures spatial information from multi-phase CT scans [61]	Renal cell carcinoma pathological grading
Synthetic Lesion Generators (GANs)	Creates artificial minority class samples to address imbalance [58]	Breast cancer classification with limited malignant cases
Stratified K-Fold Cross-Validation	Maintains class proportions across data splits [57]	Robust evaluation on imbalanced datasets

Operational Relevance in Clinical Decision-Making

Translating PR Analysis to Clinical Impact

The PR curve provides directly actionable insights for clinical deployment of cancer classification models. By examining the curve, clinicians can determine the probability threshold that balances sensitivity and precision according to clinical priorities [53]. This threshold selection corresponds to concrete operational metrics:

• Number Needed to Alert (NNA): Defined as 1/PPV, NNA represents how many alerts clinicians must respond to for each correct prediction [53]. A lower PPV translates to higher NNA and greater alert burden.
• Threshold Selection for Clinical Context: For life-threatening conditions where missing cases is unacceptable (e.g., cancer detection), high sensitivity (recall) is prioritized, even at the cost of lower precision [54]. For confirmatory testing or high-risk interventions, higher precision may be prioritized to minimize false positives.

In the cerebral edema prediction example, the PR curve revealed that at sensitivities near 0.90, positive predictive values ranged from just 0.15 to 0.20, corresponding to an NNA of 5–7 [53]. This means clinicians would face 5–7 false alerts for every correct prediction—a crucial consideration for implementation that was not apparent from the ROC analysis alone.

Decision Framework for Metric Selection

The following diagram provides a systematic approach for researchers to select between ROC and PR analysis based on dataset characteristics and clinical objectives:

In cancer classification research, where imbalanced datasets are the norm rather than the exception, the precision-recall curve offers a more informative and clinically relevant evaluation framework than the traditional ROC curve. By focusing on precision rather than specificity, PR analysis aligns with the operational priorities of healthcare providers—reliable identification of rare events and manageable alert burden [53]. The empirical evidence consistently demonstrates that ROC-AUC can provide misleadingly optimistic performance estimates for imbalanced problems, while PR-AUC maintains its discriminative power across imbalance levels [53] [57].

For researchers developing cancer classification models, we recommend:

• Routine PR Analysis: Include PR curves and AUPRC as standard evaluation metrics, particularly for imbalanced datasets.
• Contextual Threshold Selection: Use PR curves to select classification thresholds based on clinical priorities and operational constraints.
• Complementary Metrics: While PR analysis should be primary for imbalanced scenarios, ROC analysis remains valuable for assessing overall discrimination.
• Reporting Standards: Clearly report class imbalance ratios and use appropriate statistical methods (e.g., bootstrapping) for AUPRC confidence intervals [53].

By adopting PR analysis as a central evaluation framework, the cancer research community can develop more transparent, reliable, and clinically useful classification models that truly address the challenges of real-world medical data.

Cancer classification represents a cornerstone of modern oncology, directly influencing diagnostic accuracy, treatment selection, and patient outcomes. With the advent of sophisticated computational methods, researchers have developed increasingly refined models capable of distinguishing between cancer types and subtypes based on diverse data modalities. This guide objectively compares the performance of cutting-edge deep learning and machine learning approaches across two fundamental classification paradigms: skin cancer diagnosis using image data and pan-cancer classification utilizing molecular and transcriptomic data. By synthesizing experimental data from recent studies and detailing methodological protocols, this resource aims to inform researchers, scientists, and drug development professionals about the current state of cancer classification models and their performance metrics.

Performance Comparison of Cancer Classification Models

The table below summarizes quantitative performance metrics from recent studies on skin cancer and pan-cancer classification, enabling direct comparison of model effectiveness across different data types and clinical challenges.

Table 1: Performance Metrics of Cancer Classification Models

Study Focus	Model Architecture/Approach	Dataset	Key Performance Metrics	Clinical/Research Advantage
Skin Cancer Classification	MobileNetV2 with Memetic Algorithm Optimization [62]	Custom dataset (originally 30 benign, 240 malignant samples)	Accuracy: 98.48%, Precision: 97.67%, Recall: 100%, ROC AUC: 99.79% [62]	High performance with resource efficiency; suitable for clinical settings
Skin Cancer Classification	EfficientNetV2L with Adaptive Early Stopping [60]	ISIC dataset	Accuracy: 99.22% [60]	Handles imbalanced datasets; prevents overfitting
Skin Cancer Classification	Hybrid LSTM-CNN Model [63]	HAM10000 (10,015 images)	Outperformed baseline models across accuracy, recall, precision, F1-score, and ROC curves [63]	Captures both spatial features and temporal dependencies in lesion images
Skin Cancer Classification	ResNet50-LSTM with Transfer Learning [64]	Multiple public datasets	Accuracy: 99.09% [64]	Combines deep feature extraction with sequential pattern recognition
Pan-Cancer Classification	PC-RMTL (Regularized Multi-Task Learning) [65]	TCGA RNASeq data (21 cancer types)	Accuracy: 96.07%, MCC: 95.80% [65]	Effectively classifies 21 cancer types and normal samples; handles cross-cancer feature learning
Pan-Cancer Classification	ShuffleNet for Histology-Based Inference [66]	TCGA (14 tumor types, 5,000+ patients)	AUROC range: 0.60-0.78 for detectable genetic alterations [66]	Infers genetic mutations directly from H&E stained histology slides; mobile-friendly
Pan-Cancer Classification	SVM with Linear Kernel [65]	TCGA RNASeq data (21 cancer types)	Accuracy nearly equal to PC-RMTL with full feature set [65]	Strong performance on complete molecular feature sets
Breast Cancer Classification	Ensemble Methods (AdaBoost, GBM, RGF) [67]	Clinical and genomic breast cancer data	Accuracy: 99.5% [67]	Combines multiple classifiers for enhanced performance

Detailed Experimental Protocols

Skin Cancer Classification with MobileNetV2 and Memetic Algorithm Optimization

Dataset Preparation and Preprocessing: The initial dataset contained highly imbalanced classes with only 30 benign samples compared to 240 malignant samples. To address this, researchers applied comprehensive data augmentation techniques to the benign class using an ImageDataGenerator with the following parameters: rescale (1./255), rotationrange (20), widthshiftrange (0.2), heightshiftrange (0.2), shearrange (0.2), zoomrange (0.2), horizontalflip (True), and fill_mode ('nearest'). This process increased the benign sample size to 200, creating a more balanced dataset. All images were resized to 224×224 pixels, converted to tensors, and normalized using ImageNet's standard mean and standard deviation. The dataset was then divided using a random split into training (70%), validation (15%), and testing (15%) sets, ensuring representative distribution of both classes in each split [62].

Model Architecture and Training: The framework utilized MobileNetV2, a lightweight convolutional neural network architecture designed for mobile and edge devices. Key innovations in MobileNetV2 include depthwise separable convolutions that decouple spatial filtering from feature processing, significantly reducing model parameters and computational complexity. The architecture also employs inverted residual blocks that expand feature dimensionality before applying depthwise convolutions, then reduce dimensionality while preserving critical features. The model was initially pretrained on ImageNet to leverage transfer learning, then fine-tuned on the skin cancer dataset [62].

Memetic Algorithm for Hyperparameter Optimization: The memetic algorithm combined global and local search techniques to optimize hyperparameters including learning rate, batch size, and number of epochs. The global search phase employed genetic algorithms with selection, crossover, and mutation operations to explore a broad solution space. Candidate solutions were evaluated based on validation set performance. Promising candidates then underwent local refinement through iterative optimization techniques to fine-tune hyperparameters for maximum performance [62].

Table 2: Research Reagent Solutions for Skin Cancer Classification

Research Reagent	Function/Application	Specification/Parameters
MobileNetV2 Architecture	Feature extraction from dermoscopic images	Depthwise separable convolutions; inverted residual blocks; linear bottlenecks [62]
Memetic Algorithm	Hyperparameter optimization	Combines genetic algorithms (global search) with local refinement techniques [62]
ImageDataGenerator	Data augmentation for class imbalance	Rotation: 20°; width/height shift: 20%; shear: 20%; zoom: 20%; horizontal flip: True [62]
Grad-CAM Visualization	Model interpretability	Generates heatmaps highlighting regions of interest in lesion images [62]
PyTorch Framework	Model development and training	Custom Dataset class for data management; normalization transforms [62]

Pan-Cancer Classification with Regularized Multi-Task Learning

Data Collection and Preprocessing: The study utilized RNASeq transcript abundance counts of 56,493 Ensembl genes for 21 cancer types from The Cancer Genome Atlas (TCGA). The dataset included primary tumor and adjacent normal tissue samples, totaling 7,839 samples across 22 classes (21 cancer types plus one normal class). Researchers identified differentially expressed (DE) genes for each cancer type using DESeq2 R package, applying thresholds of adjusted p-value ≤ 0.05 and fold-change ≥ 2. The union set of the top 75 DE genes from each cancer type yielded 1,055 highly significant genes for the classification task. Variance stabilizing transformation (VST) was applied to normalize raw transcript abundance counts before model training [65].

PC-RMTL Framework: The Pan-Cancer Regularized Multi-Task Learning (PC-RMTL) framework was designed to learn the RNASeq gene expression patterns of multiple cancer types simultaneously by leveraging relationships between tasks. The model minimized logistic loss across all classification tasks while incorporating regularization terms to control model complexity and enhance generalization. This approach allowed the model to share information across cancer types while maintaining task-specific discriminative capabilities. The framework was compared against five state-of-the-art classifiers: SVM with linear kernel, SVM with radial basis function kernel, random forest, k-nearest neighbors, and decision trees [65].

Evaluation Methodology: Model performance was assessed using three-fold cross-validation and evaluated on a completely independent test set. Metrics included precision, recall, F1-score, Matthews correlation coefficient (MCC), ROC curves, precision-recall curves, and logistic loss. The study also evaluated performance with reduced feature sets selected using SVM coefficients and minimum redundancy maximal relevance (MRMR) algorithm [65].

Figure 1: PC-RMTL Pan-Cancer Classification Workflow. The diagram illustrates the sequential steps in the regularized multi-task learning approach for classifying 21 cancer types using RNASeq data [65].

Advanced Classification Approaches

Hybrid Deep Learning Models for Skin Cancer

Recent advances in skin cancer classification have explored hybrid architectures that combine complementary deep learning approaches. The LSTM-CNN model processes each skin lesion image as a sequence of patches, using LSTM networks to capture temporal dependencies and spatial relationships between different regions. The CNN components then extract spatial features including texture, edges, and color variations from these sequences. This approach has demonstrated superior performance in handling the diversity and complexity of skin lesions compared to models using either CNNs or LSTMs alone [63].

Similarly, the ResNet50-LSTM model with transfer learning leverages pre-trained features from large datasets, analyzes sequential patterns in medical images, and fine-tunes the combined architecture specifically for skin cancer classification. This method addresses common challenges such as class imbalance through multi-attention mechanisms and achieves exceptional accuracy exceeding 99% on benchmark datasets [64].

Histology-Based Genotype Inference

A groundbreaking approach in pan-cancer analysis involves predicting molecular alterations directly from routine histology slides using deep learning. The optimized ShuffleNet architecture demonstrates that genetic mutations, molecular subtypes, and gene expression signatures can be inferred from hematoxylin and eosin (H&E) stained tissue sections across multiple solid tumors. This method successfully predicted clinically actionable genetic alterations including TP53, BRAF, PIK3CA, and microsatellite instability (MSI) status with statistically significant AUROC scores ranging from 0.60 to 0.78 across different cancer types [66].

Figure 2: Histology-Based Genotype Inference. The diagram illustrates how deep learning connects histology morphology with molecular alterations in cancer, enabling prediction of genetic mutations from routine tissue slides [66].

The comparative analysis presented in this guide demonstrates significant advances in both skin cancer and pan-cancer classification methodologies. Deep learning approaches consistently achieve high performance metrics, with specialized architectures optimized for specific data types and clinical challenges. The experimental protocols and reagent solutions detailed herein provide researchers with practical frameworks for implementing these models in their own work. As cancer classification models continue to evolve, the integration of multi-modal data, improved interpretability features, and resource-efficient architectures will further enhance their utility in both research and clinical environments. The performance metrics established in these studies serve as benchmarks for future development in the field of computational oncology.

Navigating Challenges: Optimizing Metric Selection for Clinical and Research Goals

Within computational oncology, the performance of a cancer classification model is not merely an academic statistic but a determinant of clinical translation. While accuracy offers a superficial measure of success, the precision-recall (PR) trade-off provides a more nuanced lens, directly linking model decisions to clinical consequences. This trade-off forces a critical evaluation: is it costlier to miss a cancer (false negative) or to incorrectly flag a healthy patient (false positive)? The answer varies dramatically across clinical scenarios, from early screening of rare cancers to the precise subtyping of known tumors.

This guide objectively compares the application and optimization of this trade-off across contemporary cancer classification studies, providing researchers with the methodological framework to align model evaluation with clinical priority.

Core Concepts and Clinical Relevance

Defining the Metrics

In the context of cancer classification, precision and recall are defined using the core components of a confusion matrix [68]:

• Precision answers: "Of all patients the model predicted have cancer, how many actually do?" It is defined as True Positives (TP) divided by the sum of True Positives and False Positives (FP). A high precision means that when the model flags a case as positive, you can be highly confident it is correct, thereby minimizing false alarms.
• Recall (also known as Sensitivity) answers: "Of all patients who truly have cancer, how many did the model successfully find?" It is defined as True Positives (TP) divided by the sum of True Positives and False Negatives (FN). A high recall means the model is missing very few actual cancer cases.

The following diagram illustrates the fundamental inverse relationship between these two metrics.

The Clinical Cost of Errors

The relative cost of a False Positive versus a False Negative dictates whether one should optimize for precision or recall [69] [70].

• Prioritizing Recall is critical in scenarios where missing a positive case (False Negative) has severe, irreversible consequences. This is paramount in early cancer screening programs (e.g., for lung or brain cancer) and in triaging cases for further expert review. Here, the cost of a missed early-stage tumor far outweighs the cost of additional follow-up tests for healthy patients [71].
• Prioritizing Precision is essential when a False Positive leads to unnecessary, invasive, costly, or risky procedures. For instance, in confirming cancer subtypes from genomic data to determine eligibility for a specific targeted therapy, an incorrect classification could steer a patient toward an ineffective and toxic treatment [72].
• Balancing Both Metrics is often required when both error types carry significant cost. In payment fraud prediction, analogous to predicting aggressive cancer types, failing to catch a positive case (FN) and incorrectly flagging a negative case (FP) are both undesirable. The F1-score, the harmonic mean of precision and recall, is a single metric often used to balance these concerns [70].

Comparative Analysis in Cancer Classification

The table below summarizes how the precision-recall trade-off is managed in recent cancer classification studies, highlighting the direct impact of clinical context on model design and evaluation.

Table 1: Precision-Recall Trade-off in Recent Cancer Classification Studies

Study / Model	Cancer Type(s)	Primary Data Type	Key Performance Metrics	Trade-off Strategy & Clinical Rationale
OncoChat (LLM) [72]	69 tumor types, Cancers of Unknown Primary (CUP)	Targeted panel sequencing (SNVs, CNAs, SVs)	PRAUC: 0.810, Accuracy: 0.774, F1: 0.756	Prioritizes high precision and recall (high PRAUC) for complex CUP diagnoses where both misdiagnosis (FP) and missed identification (FN) are critical.
RNA-Seq Classifiers [18]	5 types (BRCA, KIRC, COAD, etc.)	RNA-seq gene expression	Accuracy: 99.87% (SVM), Precision, Recall, F1	Employs a suite of metrics. The extreme accuracy suggests a balanced, high-performing model on a well-defined classification task.
DSSCC-Net (CNN) [73]	Skin cancer (7 lesion types)	Dermoscopic images	Accuracy: 97.82%, Precision: 97%, Recall: 97%	Achieves a near-perfect balance (F1 ~97%) for clinical decision support, where neither false alarms nor missed lesions are acceptable. Uses SMOTE-Tomek to address class imbalance.
DNA-Based Predictor [74]	5 types (BRCA, KIRC, etc.)	DNA sequences	Accuracy: up to 100% for some types, ROC AUC: 0.99	Uses ROC AUC, which is less sensitive to class imbalance. High performance suggests a robust classifier, but PR curves might offer more insight if classes are imbalanced.
Brain Tumor CNN [71]	Glioma, Meningioma, Pituitary	MRI scans	Accuracy: 96.95%, analysis of Loss for certainty	Focuses on certainty-aware classification, implicitly optimizing the trade-off to minimize high-confidence errors (both FP and FN) in a high-stakes domain.

Experimental Protocols and Methodologies

A Generalized Workflow for Optimizing the PR Trade-off

The following diagram and protocol outline the standard methodology for evaluating and optimizing the precision-recall trade-off, as applied in the cited studies [75] [18] [72].

Step 1: Model Training and Probability Generation The process begins with training a probabilistic classification model. Unlike models that output a final class label directly, algorithms like Logistic Regression, Random Forests, and Support Vector Machines (with predict_proba or similar methods) output a continuous score or probability for each sample belonging to a class [75] [18]. For example, a model might predict that a tissue sample has a 0.85 probability of being malignant.

Step 2: Threshold Variation and Metric Calculation A classification threshold is applied to convert probabilities into class labels. The default is often 0.5. To analyze the trade-off, this threshold is varied across a range (e.g., from 0 to 1 in 100 steps). For each threshold value:

• Predictions with scores ≥ the threshold are classified as positive.
• Precision and recall are calculated against the true labels [75] [70].

Step 3: PR Curve Generation and Analysis The precision and recall values for all thresholds are plotted to create a Precision-Recall Curve (PR Curve). A curve that bows towards the top-right corner indicates better performance. The Area Under the PR Curve (PRAUC or AP) is a key summary metric; a perfect classifier has a PRAUC of 1.0 [69] [68]. This curve visually encapsulates the trade-off.

Step 4: Optimal Threshold Selection The "optimal" point on the PR curve is selected based on the clinical or research context [70]:

• Maximizing F1-score: Finds the threshold that best balances precision and recall equally.
• Meeting a Minimum Recall: Ensures a specific level of sensitivity is achieved (e.g., recall must be >0.95).
• Meeting a Minimum Precision: Ensures predictions meet a specific level of reliability.

Step 5: Model Deployment The final, validated model is deployed using the chosen optimal threshold for inference on new data [75].

Case Study: OncoChat for Tumor Classification

The OncoChat study provides a robust example of this protocol in practice [72]:

• Data: 158,836 tumors sequenced with targeted gene panels, integrating single-nucleotide variants, copy number alterations, and structural variants.
• Model: A large language model (LLM) architecture trained to interpret genomic "sentences".
• Evaluation: The model was evaluated using a 70/30 train-test split and its performance was benchmarked against existing models (OncoNPC, GDD-ENS). The primary metric for comparison was the Precision-Recall Area Under the Curve (PRAUC), which was calculated to be 0.810.
• Rationale: For classifying Cancers of Unknown Primary (CUP), both accurately identifying the tissue of origin (high recall) and being correct in that identification (high precision) are critical for guiding life-saving, site-specific therapies. Therefore, optimizing for PRAUC, which considers both metrics across all thresholds, was the most relevant strategy.

Research Reagent Solutions

The following table details key computational tools and metrics essential for conducting precision-recall analysis in cancer classification research.

Table 2: Essential Research Reagents for PR Trade-off Analysis

Reagent / Tool	Type	Primary Function	Example in Cited Research
Scikit-learn (Python)	Software Library	Provides functions for calculating metrics, plotting PR curves, and computing PRAUC.	Used across studies [75] [18] [69] for precision_recall_curve, precision_score, and recall_score.
PRAUC (Area Under the PR Curve)	Evaluation Metric	Summarizes the overall performance of a model across all thresholds; ideal for imbalanced datasets.	The core metric reported by OncoChat (0.810) to demonstrate superiority over baselines [72].
F1-Score	Evaluation Metric	The harmonic mean of precision and recall; provides a single score to balance both concerns.	Reported alongside precision and recall in skin cancer (DSSCC-Net [73]) and brain tumor classification [71].
SMOTE-Tomek	Data Preprocessing	A hybrid sampling technique to address class imbalance, which severely impacts PR analysis.	Used by DSSCC-Net on the HAM10000 dataset to improve minority class detection without data leakage [73].
Validation Framework (e.g., k-Fold)	Experimental Protocol	Ensures robust performance estimation and prevents overfitting during threshold tuning.	Used as a 5-fold or 10-fold cross-validation in RNA-seq [18] and DNA-based classifiers [74].

In cancer research, the selection of performance metrics is not a one-size-fits-all endeavor. The choice between screening and confirmatory diagnostic contexts fundamentally shapes the optimal metric strategy, with significant implications for both patient outcomes and research validity. Screening environments prioritize the efficient identification of potential cancer cases from large populations, often requiring high sensitivity to avoid missed diagnoses. In contrast, confirmatory diagnostics demand high specificity to definitively confirm cancer presence and type, guiding critical treatment decisions. This guide examines how these distinct contexts shape metric selection, supported by experimental data and methodological considerations from recent research.

The evolving landscape of cancer diagnostics now incorporates diverse technological approaches, from high-throughput genomic sequencing to functional physiological assessments. RNA sequencing data analyzed with machine learning has demonstrated remarkable classification capabilities for specific cancer types [18]. Simultaneously, research into heart rate variability (HRV) analysis suggests that autonomic dysfunction markers may provide complementary screening information [76]. Each modality carries distinct implications for metric selection based on its underlying technology and intended use case.

Quantitative Metric Comparison Tables

Performance Metrics Across Algorithm Types

Table 1: Classification performance of machine learning algorithms on RNA-seq data

Algorithm	Reported Accuracy	Best Use Context	Key Strengths	Experimental Validation
Support Vector Machine (SVM)	99.87% [18]	Confirmatory diagnostics	High precision in high-dimensional data	5-fold cross-validation, 70/30 train-test split
Random Forest	83% [76]	Preliminary screening	Robust to noise, feature importance ranking	5-minute ECG recordings, recursive feature elimination
Ensemble Methods	86% [76]	Screening applications	Improved robustness through model stacking	HRV analysis with stacking classifier
Artificial Neural Networks	99.4% [77]	Confirmatory diagnostics	Handles complex nonlinear relationships	Two-step feature selection, 15-neuron network

Assessment Metrics for Different Diagnostic Contexts

Table 2: Core metrics and their contextual appropriateness

Metric	Screening Context	Confirmatory Context	Key Considerations	Potential Biases
Sensitivity	Critical priority	Secondary importance	Accuracy assessment interval length affects estimates [78]	Follow-up duration impacts cancer detection rates
Specificity	Secondary importance	Critical priority	Minimizes false positives in definitive diagnosis	Verification bias when gold standard not uniformly applied
Accuracy	Useful but incomplete	Useful but incomplete	Can be misleading with imbalanced classes	Varies with disease prevalence in population
F1 Score	Valuable for balance	Less frequently prioritized	Balances precision and recall for screening	Depends on sensitivity/specificity tradeoffs

Experimental Protocols and Methodologies

High-Accuracy Cancer Classification Using RNA-Seq Data

Research applying machine learning to RNA-sequencing data exemplifies rigorous methodology for confirmatory diagnostic development. The protocol encompasses several critical phases:

• Data Acquisition and Preprocessing: The PANCAN dataset from UCI Machine Learning Repository containing 801 cancer tissue samples across 5 cancer types (BRCA, KIRC, COAD, LUAD, PRAD) with expression data for 20,531 genes [18]. Data preprocessing included checking for missing values and outliers, with no missing values reported in the dataset.

• Feature Selection: Implementation of Lasso (L1 regularization) and Ridge (L2 regularization) regression to identify dominant genes amid high dimensionality and noise. Lasso regression was particularly valuable for its feature selection capability, driving irrelevant coefficients to exactly zero [18].

• Model Training and Validation: Eight classifiers were evaluated—Support Vector Machines, K-Nearest Neighbors, AdaBoost, Random Forest, Decision Tree, Quadratic Discriminant Analysis, Naïve Bayes, and Artificial Neural Networks. Validation employed both 70/30 train-test split and 5-fold cross-validation [18].

This methodology achieved exceptional classification accuracy (99.87% with SVM under 5-fold cross-validation), demonstrating the potential for genomic approaches in confirmatory diagnostics where maximum accuracy is essential [18].

Practical Screening Using Heart Rate Variability Analysis

A pilot study exploring cancer screening via autonomic dysfunction assessment exemplifies a different methodological approach tailored to screening contexts:

• Participant Recruitment and ECG Recording: The study included 77 cancer patients (breast, prostate, colorectal, lung, and pancreatic cancer across stages I-IV) and 57 healthy controls. Exclusion criteria included diabetes, cardiovascular pathologies, pregnancy, and psychiatric disorders [76].

• HRV Feature Extraction: Researchers selected 12 HRV features based on previous research, including time-domain measures (SDNN, RMSSD, pNN50%), frequency-domain measures, and non-linear measures [76].

• Feature Selection and Model Development: Recursive Feature Elimination (RFE) identified the top five features: SDNN, RMSSD, pNN50%, HRV triangular index, and SD1. These were used as input to three machine learning classifiers: Random Forest, Linear Discriminant Analysis, and Naive Bayes [76].

The ensemble model demonstrated 86% classification accuracy with an AUC of 0.95, representing a promising approach for non-invasive screening where perfect accuracy is sacrificed for practical implementation [76].

Workflow Comparison: High-accuracy confirmatory diagnostics versus practical screening approaches

The Impact of Assessment Intervals on Metric Validity

A critical methodological consideration in screening contexts is the accuracy assessment interval—the period after a screening test used to estimate its accuracy. Research indicates that the length of this interval significantly impacts sensitivity estimates, while specificity remains relatively stable [78].

For example, studies of mammography sensitivity demonstrated notable differences when using 2-year versus 1-year follow-up intervals (74.9% vs. 82.0%, respectively) [78]. Similarly, research on fecal occult blood testing for colorectal cancer showed sensitivities of 50%, 43%, and 25% using 1-year, 2-year, and 4-year follow-up periods, respectively [78]. This interval effect creates an inherent tradeoff: shorter intervals may miss cancers that were truly present at screening, while longer intervals may incorrectly classify new cancers as having been present initially.

The relationship between assessment interval and metric validity can be visualized as follows:

Assessment interval impact on screening metric validity

Essential Research Reagent Solutions

Table 3: Key research materials and computational tools for diagnostic development

Resource Category	Specific Tools/Platforms	Research Application	Function in Development
Genomic Data Resources	TCGA PANCAN dataset [18]	Confirmatory model training	Provides comprehensive RNA-seq data across cancer types
Bioinformatics Tools	Lasso & Ridge Regression [18]	Feature selection	Identifies significant genes amid high-dimensional noise
Physiological Recording	ECG Holter Monitoring (MedilogAR) [76]	HRV data acquisition	Captures cardiac signals for autonomic function assessment
Validation Frameworks	5-fold Cross-validation [18]	Model performance assessment	Provides robust accuracy estimation while mitigating overfitting
Metric Assessment	Accuracy Assessment Interval [78]	Screening test evaluation	Defines appropriate follow-up for sensitivity/specificity calculation

The selection of appropriate metrics in cancer diagnostics requires careful consideration of the clinical and research context. Confirmatory diagnostics, exemplified by RNA-seq classification approaches, demand maximum accuracy and robust validation through cross-validation techniques [18]. In contrast, screening applications must prioritize practical implementation with attention to how assessment intervals impact sensitivity estimates [78].

Emerging approaches like HRV analysis demonstrate how alternative modalities can provide valuable screening information when paired with appropriate metrics and expectations [76]. By aligning metric selection with diagnostic context and understanding the methodological factors that influence metric validity, researchers can develop more effective cancer classification strategies that appropriately balance sensitivity, specificity, and practical implementation requirements.

In the field of cancer classification research, the imperative to develop reliable predictive models is paramount. The performance of these models directly influences critical decisions in diagnosis, prognosis, and treatment planning. A significant challenge in this domain is the frequent occurrence of class imbalance, where one class (e.g., healthy patients) vastly outnumbers the other (e.g., cancer patients) [38] [79]. In such scenarios, common metrics like overall accuracy become misleading, as a model could achieve high accuracy by simply always predicting the majority class, thereby failing to identify the critical minority class [80] [81].

This guide provides an objective comparison of two metrics specifically designed to offer a more truthful evaluation in imbalanced contexts: the Matthews Correlation Coefficient (MCC) and Balanced Accuracy (BA). The focus is framed within cancer classification research, where the cost of misclassification—particularly a false negative—can be extraordinarily high [79]. We will dissect their mathematical foundations, present comparative experimental data, and detail their applications to empower researchers in selecting the most informative metric for their work.

Metric Definitions and Mathematical Foundations

Matthews Correlation Coefficient (MCC)

The Matthews Correlation Coefficient is a metric that evaluates the quality of binary classifications by considering all four entries of the confusion matrix: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) [82]. It is calculated as the geometric mean of all four confusion matrix rates and is essentially a correlation coefficient between the observed and predicted binary classifications [83] [84].

The formula for MCC is: $$MCC = \frac{(TP \times TN) - (FP \times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}$$

Value Range: -1 to +1 [82] [83]

• +1: Indicates a perfect prediction.
• 0: Indicates a prediction no better than random guessing.
• -1: Indicates total disagreement between prediction and actual values.

Balanced Accuracy (BA)

Balanced Accuracy is the arithmetic mean of Sensitivity (True Positive Rate) and Specificity (True Negative Rate) [83] [84]. It was introduced to overcome the skew in overall accuracy when class distributions are unequal.

The formula for BA is: $$Balanced\ Accuracy = \frac{Sensitivity + Specificity}{2} = \frac{1}{2} \left( \frac{TP}{TP+FN} + \frac{TN}{TN+FP} \right)$$

Value Range: 0 to 1 [83] [84]

• 1: Represents perfect sensitivity and specificity.
• 0.5: For a binary classification, this is equivalent to random guessing.

Table 1: Core Mathematical Properties of MCC and Balanced Accuracy

Property	Matthews Correlation Coefficient (MCC)	Balanced Accuracy (BA)
Formula	$\frac{(TP \cdot TN) - (FP \cdot FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}$	$\frac{1}{2} \left( \frac{TP}{TP+FN} + \frac{TN}{TN+FP} \right)$
Value Range	-1 to +1	0 to 1
Random Guess	0	0.5
Confusion Matrix Components	Uses all four (TP, TN, FP, FN)	Uses rates derived from all four
Invariance to Class Swapping	Yes (symmetric)	Yes (symmetric)

Theoretical and Practical Comparison

Key Strengths and Limitations

The choice between MCC and Balanced Accuracy hinges on the specific requirements of the evaluation and the nature of the dataset.

Matthews Correlation Coefficient (MCC) is widely regarded as a more comprehensive and robust metric. Its key strength lies in its balanced consideration of all four categories of the confusion matrix, making it particularly reliable for imbalanced datasets [82] [80]. A high MCC score can only be achieved if the model performs well in predicting both positive and negative classes correctly [83]. However, the metric's formula is more complex, which can be a barrier to intuitive understanding for some practitioners.

Balanced Accuracy (BA) offers a significant improvement over standard accuracy by averaging sensitivity and specificity, thus providing a simpler and more interpretable alternative [83]. Its calculation is straightforward, making it easy to compute and explain. The primary limitation of BA is that its definition relies on the basic rates (Sensitivity, Specificity), which can be undefined in extreme cases where entire rows or columns of the confusion matrix are zero, whereas MCC can be mathematically defined for all confusion matrices [83].

When to Use Each Metric

• Use MCC when: Dealing with highly imbalanced datasets common in cancer diagnosis (e.g., rare cancer prediction) [80] [79]. The model's performance on both classes is equally critical, such as when the costs of false positives and false negatives are both high [83] [81]. You need a single, reliable metric that provides a holistic view of model performance across all aspects of the confusion matrix [83].
• Use Balanced Accuracy when: A simple and intuitive metric is sufficient for a quick assessment [83]. The dataset is only mildly imbalanced, and a straightforward average of sensitivity and specificity meets evaluation needs.

Experimental Data and Performance in Cancer Research

Experimental studies in cancer research consistently demonstrate the practical implications of choosing between MCC and Balanced Accuracy, especially when data is imbalanced.

One study focusing on cancer diagnosis and prognosis evaluated various classifiers and resampling techniques on five imbalanced cancer datasets. The performance was assessed using multiple metrics, and the results underscored the challenge of imbalanced data. For instance, the baseline method without any resampling yielded a performance of 91.33%, which was significantly improved by applying hybrid resampling techniques [38]. This highlights that without appropriate metrics and techniques, model performance can be misleading.

Another compelling experiment directly compared model performance on balanced versus unbalanced histopathological image datasets for breast cancer classification. Using the InceptionV3 convolutional neural network, the study showed that for balanced data, the model achieved an accuracy of 93.55% and a recall of 99.19%. In contrast, for unbalanced data, accuracy was 89.75% and recall dropped sharply to 82.89% [85]. This significant drop in recall (sensitivity) on the unbalanced data, which still retained a deceptively high accuracy, perfectly illustrates why accuracy is misleading and why metrics like BA and MCC are needed. While this study reported accuracy and recall, it reinforces the context in which BA and MCC provide a more truthful assessment.

Table 2: Metric Performance in a Synthetic Binary Classification Scenario

Scenario Description	Confusion Matrix	Accuracy	F1 Score	Balanced Accuracy	MCC
Balanced & Good PerformanceTP=80, FP=10, FN=10, TN=80	[[80, 10], [10, 80]]	0.89	0.89	0.89	0.78
Imbalanced & Good PerformanceTP=160, FP=10, FN=10, TN=20	[[160, 10], [10, 20]]	0.90	0.94	0.85	0.59
Imbalanced & Poor PerformanceTP=20, FP=10, FN=160, TN=10	[[20, 10], [160, 10]]	0.15	0.19	0.15	-0.10

The data in Table 2, inspired by analyses in the literature [83] [80], reveals critical insights. In the "Imbalanced & Good Performance" scenario, Accuracy is high at 0.90, and the F1 score is even higher at 0.94. However, MCC (0.59) and, to a lesser extent, Balanced Accuracy (0.85) provide a more conservative and realistic assessment by factoring in the model's weaker performance on the negative class. In the "Imbalanced & Poor Performance" scenario, Accuracy and F1 are low, but MCC is negative, correctly indicating that the model's predictions are worse than random guessing.

Experimental Protocols for Metric Evaluation

To ensure the rigorous evaluation of classification models using MCC and BA, researchers should adhere to a standardized workflow. The following diagram and protocol outline a robust methodology commonly employed in cancer classification studies [17] [38].

Diagram 1: Workflow for metric evaluation.

Detailed Protocol Description

• Data Collection and Pre-processing: Begin by gathering a relevant cancer dataset. Common sources in pan-cancer research include The Cancer Genome Atlas (TCGA) or SEER databases, which provide multi-omics data such as mRNA expression, miRNA expression, and copy number variation (CNV) [17]. Data pre-processing involves cleaning, normalization, and feature selection to ensure data quality.

• Address Class Imbalance: Apply resampling techniques to mitigate class imbalance. As demonstrated in research, methods like SMOTEENN (a hybrid method) have achieved performance improvements up to 98.19% in mean performance on cancer datasets, significantly outperforming models without resampling (91.33%) [38]. Alternative techniques include random undersampling (RUS) or synthetic oversampling (SMOTE) [79].

• Model Training and Hyperparameter Tuning: Partition the data into training and testing sets. Train a chosen classifier (e.g., Random Forest, which has shown mean performance of 94.69% in comparative cancer studies [38]). Optimize model hyperparameters using cross-validation on the training set to prevent overfitting.

• Generate Predictions and Confusion Matrix: Use the trained model to generate predictions on the held-out test set. From these predictions and the true labels, construct the confusion matrix, tabulating TP, TN, FP, and FN.

• Calculate Performance Metrics: Compute both MCC and Balanced Accuracy from the confusion matrix values using their respective formulas.

• Compare Metrics and Interpret Results: Analyze the results from both metrics. A high MCC score indicates strong overall performance across both classes. A significant discrepancy between high BA and a lower MCC may suggest that while the model handles class-wise accuracy well, there might be issues with the correlation between predictions and actual labels, often influenced by the pattern of errors in the confusion matrix [83].

Essential Research Reagents and Computational Tools

The following table details key resources used in computational experiments for cancer classification, as cited in the literature.

Table 3: Key Research Reagents and Tools for Cancer Classification Experiments

Item Name	Type	Function in Research	Example/Citation
TCGA (The Cancer Genome Atlas)	Data Repository	Provides comprehensive multi-omics data (genomics, transcriptomics) from over 11,000 tumor samples for pan-cancer analysis. [17]	Pan-Cancer Atlas [17]
SEER Database	Data Repository	Offers clinical and demographic information crucial for evaluating cancer outcomes and prognosis. [38]	Seer Breast Cancer Dataset [38]
SMOTEENN	Algorithm (Hybrid Resampling)	Combines oversampling (SMOTE) and undersampling (ENN) to effectively balance imbalanced cancer datasets. [38]	Achieved 98.19% mean performance [38]
Random Forest	Algorithm (Classifier)	A robust ensemble learning method frequently used as a top-performing classifier in cancer studies. [38]	Achieved 94.69% mean performance [38]
scikit-learn	Software Library	A popular Python library providing implementations for metrics like matthews_corrcoef and classifiers. [82]	sklearn.metrics module [82]

In the critical field of cancer classification, the choice of evaluation metric is not merely a technicality but a fundamental aspect of model validation. While both Balanced Accuracy and the Matthews Correlation Coefficient offer vast improvements over naive metrics like accuracy, they serve different needs.

For a quick, intuitive assessment of class-wise performance, particularly in contexts of mild imbalance, Balanced Accuracy is a valid choice. However, for a comprehensive, robust, and reliable single-value metric that is invariant to class imbalance and reflects the true quality of a binary classification, the Matthews Correlation Coefficient is superior [83] [80]. Its ability to incorporate all facets of the confusion matrix makes it the recommended metric for rigorous evaluation, especially in high-stakes applications like cancer diagnosis and prognosis where the cost of error is high. Researchers are encouraged to adopt MCC as a standard in their reporting to ensure their models are evaluated truthfully and effectively.

The integration of histopathology, genomics, and radiomics represents a transformative frontier in computational oncology, promising to revolutionize cancer classification, prognosis, and therapeutic decision-making. This multi-modal approach leverages complementary data types: histopathology provides detailed cellular and tissue-level morphological information, genomics reveals underlying molecular alterations, and radiomics offers non-invasive mesoscopic characterization of tumor phenotype and heterogeneity [86] [87]. However, the fusion of these fundamentally different data modalities creates significant challenges in metric selection and interpretation due to their divergent scales, dimensionalities, and biological meanings. The critical research question is how to quantitatively evaluate and compare the performance of these integrated models in a way that reflects their clinical utility and biological plausibility.

This guide systematically compares evaluation frameworks for multi-modal cancer classification models, with a specific focus on how they address the inherent data heterogeneity across modalities. We analyze experimental protocols, performance metrics, and computational tools that enable meaningful comparison across diverse architectural approaches to data fusion.

Performance Metrics Landscape for Multi-Modal Integration

Standard Classification Metrics

The evaluation of cancer classification models typically employs a core set of metrics, each providing distinct insights into model performance across different clinical scenarios.

Table 1: Standard Classification Metrics for Cancer Models

Metric	Calculation	Clinical Interpretation	Optimal Context
Accuracy	(TP+TN)/(TP+TN+FP+FN)	Overall correctness across all classes	Balanced class distributions
Precision	TP/(TP+FP)	Proportion of positive identifications that are correct	When false positives are clinically costly
Recall (Sensitivity)	TP/(TP+FN)	Ability to identify all relevant cases	When false negatives have severe consequences
F1-Score	2×(Precision×Recall)/(Precision+Recall)	Harmonic mean of precision and recall	Class-imbalanced datasets
AUC-ROC	Area under ROC curve	Overall diagnostic ability across all thresholds	Threshold-independent performance assessment

For binary classification tasks in oncology, such as distinguishing malignant from benign tumors, studies have reported pooled AUC values of 0.86 (95% CI: 0.83-0.88), with accuracy of 0.83 (95% CI: 0.78-0.88), sensitivity of 0.80 (95% CI: 0.75-0.84), and specificity of 0.84 (95% CI: 0.80-0.88) in radiomics-based hepatocellular carcinoma diagnosis [88]. In pan-cancer genomic classification, image-based deep learning models have achieved overall accuracy exceeding 95% across 36 cancer types [89].

Advanced Metrics for Multi-Modal Scenarios

Beyond standard classification metrics, specialized measures are required to evaluate how effectively models integrate information across modalities.

Table 2: Advanced Metrics for Multi-Modal Integration

Metric Category	Specific Metrics	Purpose in Multi-Modal Context	Reported Performance
Clustering Quality	Normalized Mutual Information (NMI), Adjusted Rand Index (ARI)	Measures agreement between discovered subtypes and known classifications	NMI >0.6, ARI >0.5 in successful subtyping [19]
Cross-Modal Alignment	Canonical Correlation Analysis (CCA), Modality Gap measurements	Quantifies how well representations from different modalities align	Used in validation but rarely reported quantitatively
Model Calibration	Expected Calibration Error (ECE), Brier Score	Measures how well-predicted probabilities match true probabilities	Critical for clinical decision support
Interpretability	Faithfulness, Comprehensiveness	Quantifies explanation quality for multi-modal predictions	Emerging area with limited benchmarks

In multi-task frameworks that jointly predict histology features and molecular markers, studies have demonstrated state-of-the-art performance on classifying glioma types and associated biomarkers, though specific metric values are highly task-dependent [90].

Experimental Protocols for Multi-Modal Benchmarking

Data Preparation and Preprocessing Standards

The foundation of reliable multi-modal cancer classification lies in standardized data processing protocols that handle modality-specific artifacts and biases.

Genomic Data Processing: RNA-seq expression data typically undergoes log2-transformation of normalized read counts, with values less than 1 set to 1 to reduce noise [91]. Feature selection often employs ANOVA testing with Benjamini-Hochberg correction to control false discovery rate, followed by z-score normalization [19]. For mutation-based classification, genetic variant data can be converted into genetic mutation maps suitable for image-based deep learning approaches [89].

Histopathology Image Processing: Whole Slide Images (WSIs) require multi-scale feature extraction, from high-magnification cellular-level features to low-magnification tissue-level patterns [90]. Standard preprocessing includes stain normalization, tissue segmentation, and patch extraction at multiple resolutions (e.g., 128×128 pixels to 512×512 pixels) [27] [92]. Data augmentation techniques such as flips, small rotations, and brightness jitter help address class imbalance [27].

Radiomics Feature Extraction: Medical images (MRI, CT, PET) undergo tumor segmentation followed by computational feature extraction including texture analysis (gray-level co-occurrence matrices), shape descriptors, and intensity statistics [86] [87]. Biologically inspired features capture specific tumor characteristics like heterogeneity and spatial organization [87]. Test-retest and interobserver stability analyses are critical for ensuring feature robustness [87].

The experimental workflow for multi-modal integration follows a structured pipeline from data acquisition to model evaluation:

Model Validation Strategies

Robust validation is particularly challenging in multi-modal settings due to data heterogeneity and limited sample sizes.

Validation Protocols: Studies consistently emphasize the importance of external validation, though it remains underutilized (only 17% of radiomics-AI studies conducted external validation) [86]. The recommended approach employs stratified patient-level splits with 75-15-10% ratios for training, validation, and testing, maintaining class distributions across splits [27] [91]. For small datasets, nested cross-validation provides more reliable performance estimates.

Baseline Establishment: Meaningful benchmarking requires comparison against established unimodal baselines. For genomic classification, Logistic Regression, Random Forests, and Support Vector Machines remain strong benchmarks [19]. In histopathology, CNN architectures like VGG-16, ResNet-50, and Inception-v3 provide reference points [27] [89]. Radiomics models commonly use LASSO feature selection combined with Random Forests or SVM classifiers [88].

Multi-Modal Fusion Techniques: Integration strategies span early fusion (feature concatenation), intermediate fusion (shared representations), and late fusion (decision-level integration). Attention-based hierarchical multi-task learning has shown promise for jointly modeling histology and molecular markers [90]. Cross-modal interaction modules with dynamic confidence constraints help balance modality contributions during training [90].

Comparative Performance Analysis Across Modalities

Unimodal versus Multi-Modal Performance

Direct comparisons between unimodal and multi-modal approaches demonstrate the value of integration, though gains vary by cancer type and clinical task.

Table 3: Performance Comparison Across Modalities

Model Type	Cancer Type	Best Performing Algorithm	Reported Performance	Clinical Task
Genomics-Only	Pan-Cancer (31 types)	GA/KNN	>90% accuracy	Tumor type classification [91]
Histopathology-Only	Breast Cancer	Custom CNN Ensemble	97.50% accuracy (4-class)	Histopathological subtyping [92]
Radiomics-Only	Hepatocellular Carcinoma	LASSO + Logistic Regression	AUC: 0.86	Diagnosis [88]
Radiomics+AI	Bone/Soft Tissue Tumors	Random Forests (42%), CNNs (17%)	Median AUC: 0.82-0.91	Tumor grading, therapy response [86]
Histopathology+Molecular	Glioma	Attention-based Multi-task Learning	State-of-the-art (specific metrics not provided)	Integrated classification [90]

The table reveals that while unimodal approaches can achieve high performance (90-97.5% accuracy), multi-modal integration provides more comprehensive tumor characterization, particularly for complex tasks like therapy response prediction and molecular subtyping.

Cross-Cancer Generalizability

Models vary significantly in their ability to maintain performance across different cancer types and datasets. Pan-cancer genomic classifiers demonstrate remarkable generalizability, correctly classifying >90% of samples across 31 tumor types using expression patterns of just 20 genes [91]. Histopathology models like CancerDet-Net show strong cross-cancer capability, achieving 98.51% accuracy across nine histopathological subtypes from four major cancer types (lung, colon, skin, breast) [27].

Radiomics models face greater generalizability challenges due to institutional differences in imaging protocols and scanners. Studies note "limited external validation" as a critical gap, with only 17% of radiomics-AI studies incorporating external validation cohorts [86].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Successful multi-modal integration requires specialized computational tools and datasets designed to handle heterogeneous oncology data.

Table 4: Essential Research Reagents for Multi-Modal Cancer Classification

Resource Category	Specific Tools/Databases	Primary Function	Key Features
Multi-Omics Databases	MLOmics [19], TCGA [91] [19]	Curated molecular data for machine learning	8,314 patients, 32 cancer types, 4 omics types
Histopathology Tools	CancerDet-Net [27], Cross-platform ViT frameworks [27]	Multi-cancer histopathology classification	Local-window self-attention, explainable AI
Radiomics Platforms	LASSO feature selection [88], Computational image descriptors [87]	Quantitative medical image analysis	Texture, shape, and intensity feature extraction
Integration Frameworks	Multi-task Multi-instance Learning [90], Cross-modal interaction modules [90]	Fusing histology and molecular data	Models co-occurrence of molecular markers
Validation Suites	Stratified cross-validation [27] [91], External validation cohorts [86]	Performance assessment and generalization testing	Statistical robustness measures

The validation process for multi-modal models requires careful consideration of both technical performance and biological plausibility:

Critical Gaps and Future Directions

Despite promising advances, significant challenges remain in metric standardization for multi-modal integration. Current literature identifies three critical gaps: (1) lack of standardized multi-omic feature fusion methods, (2) limited external validation (only 17% of studies), and (3) insufficient explainability in deep learning approaches [86]. The field shows an urgent need for attention-based neural networks and graph-based models to bridge imaging-molecular divides [86].

Future work should focus on developing modality-agnostic evaluation metrics that fairly assess contributions from each data type while accounting for their inherent heterogeneity. Additionally, the field requires consensus protocols for radiogenomic dataset sharing and benchmark establishment to enable meaningful cross-study comparisons [86] [19]. As multi-modal models move toward clinical deployment, metrics must evolve beyond pure classification accuracy to encompass computational efficiency, interpretability, and seamless integration into clinical workflows.

Ensuring Rigor: Statistical Validation and Benchmarking of Cancer Models

In the field of cancer classification research, the primary goal is to develop predictive models that generalize effectively to new, unseen patient data. These models aim to stratify cancer types, predict mutation status, or forecast therapeutic response based on various molecular data types, such as transcriptomics or exome sequences [93] [94]. The central challenge in this endeavor is overfitting—when a model learns patterns specific to the training dataset that do not generalize to new data, creating an overoptimistic performance assessment [95] [96]. This is particularly problematic in clinical applications, where model failure can directly impact patient care decisions.

Robust validation techniques are therefore not merely procedural steps but fundamental safeguards that determine whether a discovered biological signal is real or merely reflects noise in a specific dataset [97]. The holdout method and cross-validation (CV) form the cornerstone of this validation process, providing frameworks to estimate how a model will perform in real-world clinical settings [95] [98]. This guide objectively compares these techniques within the critical context of cancer research, where dataset limitations, high-dimensionality, and the need for clinical reliability present unique challenges.

Comparative Analysis of Validation Techniques

Quantitative Performance Comparison of Validation Methods

The selection of an appropriate validation strategy involves balancing computational efficiency, bias, and variance of the performance estimate. The following table summarizes these trade-offs based on empirical evaluations in biomedical research contexts:

Technique	Typical Data Split	Bias of Estimate	Variance of Estimate	Computational Cost	Ideal Use Case in Cancer Research
Hold-Out Validation	70-30 or 80-20 [95] [96]	High (as it uses only a portion of data for training) [96]	High (estimate is highly dependent on a single split) [96]	Low	Very large datasets (>10,000 samples) [95]
k-Fold Cross-Validation	k folds (commonly k=5 or k=10) [99]	Intermediate	Low to Intermediate [96]	Moderate (model is trained k times)	Medium-sized datasets; model selection [100] [99]
Stratified k-Fold CV	k folds with preserved class distribution [95] [96]	Low	Low	Moderate	Classification with imbalanced class sizes (e.g., rare cancers) [95] [96]
Leave-One-Out CV (LOOCV)	1 sample for test, n-1 for train [98]	Low [96]	High (outputs of n models are highly correlated) [96]	High (model is trained n times, once for each sample) [98]	Very small datasets (e.g., n < 100) [101] [98]
Repeated/Monte Carlo CV	Multiple random splits (e.g., 70-30) [98]	Low	Lower than standard hold-out (due to averaging) [98]	High (multiple iterations of training)	Achieving robust performance estimates without a single lucky split [98]

Experimental Evidence in Cancer Genomics

Empirical studies in cancer genomics provide critical data on how these validation techniques perform in practice. A systematic evaluation of cancer transcriptomic prediction models explored generalization across datasets (e.g., from cell lines to human tumors) and across cancer types [93]. The key finding was that model performance on held-out data via cross-validation was equally indicative of generalization as selecting smaller, supposedly more robust, gene signatures [93]. This suggests that for cancer transcriptomic model selection, simply choosing the model with the best cross-validation performance is a sound strategy, challenging the conventional wisdom that simpler models inherently generalize better.

Another study implementing ensemble machine learning algorithms on exome datasets for cancer diagnosis demonstrated the practical application of these techniques. The research used a 70:15:15 ratio for training, validation, and holdout test sets, achieving an accuracy of 82.91% on the final holdout set after model development and tuning [94]. This highlights the standard protocol of using an internal validation set (or cross-validation) for model development and a completely untouched holdout set for the final performance estimate.

Core Validation Protocols and Their Experimental Workflows

The Hold-Out Test Set Protocol

The hold-out method is the most fundamental validation technique, serving as the final arbiter of model performance before clinical deployment.

Experimental Protocol

The standard hold-out protocol involves these critical steps:

• Initial Partitioning: The entire dataset D is randomly partitioned into a training set (D_train) and a hold-out test set (D_test). For a 70-30 split, 70% of patients are assigned to D_train and 30% to D_test [96].
• Strict Separation: The hold-out test set D_test is set aside and must not be used for any aspect of model training, including feature selection or hyperparameter tuning [95] [97].
• Model Development: All model development, including feature engineering, algorithm selection, and hyperparameter tuning, is performed exclusively using D_train [95].
• Final Evaluation: The final, fully-trained model is evaluated exactly once on the hold-out test set D_test to obtain an unbiased estimate of its generalization performance [95].

A common and costly pitfall is "tuning to the test set," where researchers repeatedly modify their model based on performance on the hold-out set. This effectively leaks information from the test set into the training process, resulting in an overoptimistic performance estimate [95].

Workflow Visualization: Hold-Out Validation

The following diagram illustrates the fundamental workflow of the hold-out validation method:

k-Fold Cross-Validation Protocol

k-Fold Cross-Validation provides a more robust estimate of model performance by repeatedly splitting the training data, which is crucial when dealing with limited sample sizes common in cancer studies [100].

Experimental Protocol

The standard k-fold CV protocol, when used with a final hold-out test set, involves these steps:

• Hold-Out Separation: First, a final hold-out test set D_test is separated from the entire dataset D [95] [101]. The remaining data is the development set, D_dev.
• Shuffling and Folding: D_dev is randomly shuffled and partitioned into k folds (subsets) of approximately equal size [99]. Common values for k are 5 or 10 [99].
• Iterative Training and Validation: For each of the k iterations:
  • One unique fold is designated as the validation fold.
  • The remaining k-1 folds are combined to form the training fold.
  • A model is trained on the training fold and its performance is evaluated on the validation fold.
  • The performance score is recorded, and the model is discarded [99].
• Performance Estimation: The k performance scores are averaged to produce a single estimate of the model's performance. This average score is used for model selection or hyperparameter tuning [98] [99].
• Final Model Training: After the best model configuration is identified, a final model is trained on the entire development set D_dev [95].
• Final Testing: This final model is evaluated on the untouched hold-out test set D_test to obtain the generalization estimate [95].

It is critical to perform any data preprocessing (e.g., normalization) or feature selection within the CV loop, fitting them only on the training folds of D_dev and then applying them to the validation fold. Performing these steps prior to splitting can cause data leakage and optimistically biased results [97] [99].

Workflow Visualization: k-Fold Cross-Validation

The following diagram illustrates the iterative process of k-fold cross-validation, which provides a more robust performance estimate than a single hold-out split:

Advanced Validation Paradigms for Cancer Research

Nested Cross-Validation

Nested cross-validation (or double cross-validation) is an advanced technique used when both model selection and a robust performance estimate are required from a single dataset [100]. It consists of two layers of cross-validation: an inner loop for hyperparameter tuning and model selection, and an outer loop for performance estimation. This method provides an almost unbiased estimate of the true performance of a model with its tuned hyperparameters but is computationally very intensive [100].

Cross-Cohort and Leave-One-Dataset-Out Validation

In cancer research, a model's ability to generalize across different patient populations is paramount. Cross-cohort validation tests this directly by training a model on one cohort (e.g., from one institution or study) and testing it on a completely different cohort [97]. This can reveal whether a model has learned generalizable biological signals or merely study-specific artifacts.

A more extensive form is Leave-One-Dataset-Out CV (LODO), used when multiple datasets are available. In each iteration, the model is trained on all but one dataset and validated on the left-out dataset [97]. This is considered a gold standard for assessing generalizability in multi-center studies.

Successful implementation of robust validation techniques requires both computational tools and well-characterized data resources. The following table details key components of the validation toolkit for cancer classification research.

Resource / Reagent	Function in Validation	Specific Examples / Packages
Programming Environment	Provides the core infrastructure for data preprocessing, model training, and validation.	Python with scikit-learn [96] [99], R with caret/tidymodels
Cross-Validation Modules	Implements various CV splitters to partition data for training and validation.	sklearn.model_selection (KFold, StratifiedKFold, traintestsplit) [96]
Public Cancer Genomic Databases	Provide source data for model development and testing; enable cross-cohort validation.	The Cancer Genome Atlas (TCGA), NCBI SRA (for exome data) [94]
Clinical Data Repositories	Provide structured, real-world health data for model development and validation.	MIMIC-III [100]
Performance Metrics	Quantify model performance on classification, regression, or survival tasks.	scikit-learn metrics (accuracy, AUC), scikit-survival (C-index)
High-Performance Computing (HPC)	Reduces computation time for resource-intensive procedures like nested CV.	University/cluster HPC, Cloud computing (AWS, GCP)

In the field of cancer classification research, the development of machine learning (ML) and deep learning models has accelerated dramatically. Researchers routinely present performance metrics—often a single accuracy value or C-index—to advocate for their models. However, a solitary metric, without context or statistical validation, provides insufficient evidence for true model superiority [102] [103]. The reliance on such isolated values can lead to misleading conclusions, as apparent differences may stem from random variations in the data splitting rather than genuine algorithmic advantages [103].

Statistical tests provide the necessary framework to determine whether observed performance differences are statistically significant, offering a more rigorous foundation for scientific claims. Their application is crucial in biomedical research, where model selection can influence diagnostic tools and treatment strategies [104]. This guide examines the statistical tests that move beyond point estimates, enabling robust comparison of cancer classification models.

Foundational Concepts: From Performance Metrics to Statistical Significance

Before undertaking statistical testing, researchers must select appropriate metrics to quantify model performance. These metrics form the basis for subsequent statistical comparisons.

Classification Metrics

For binary and multi-class classification tasks—such as distinguishing malignant from benign tumors or classifying cancer subtypes—common metrics include accuracy, sensitivity (recall), specificity, precision, and the F1-score [102]. The F1-score, representing the harmonic mean of precision and recall, is particularly valuable when dealing with imbalanced class distributions common in medical datasets [102] [22].

For models that output probability scores rather than binary labels, the Area Under the Receiver Operating Characteristic Curve (AUC) provides a threshold-independent measure of discriminative ability [102]. Additionally, Cohen's Kappa (κ) accounts for agreement occurring by chance, while Matthews' Correlation Coefficient (MCC) offers a balanced measure even with significant class imbalances [102].

Survival Analysis Metrics

For time-to-event outcomes, such as predicting patient survival probabilities or time to cancer recurrence, the concordance index (C-index) is the standard metric for evaluating model discrimination [104]. It measures whether the model's predicted risk scores correctly order the actual event times.

Key Statistical Tests for Model Comparison

When comparing models, the choice of statistical test depends on the experimental design, particularly whether the comparisons are paired or unpaired, and the type of data being analyzed.

Tests for Comparing Two Models on Multiple Datasets

When evaluating two models across multiple datasets or data resamples, paired tests are required because the same data partitions are used for both models.

• 5×2-fold Cross-Validation Paired t-test: This method involves randomly splitting the dataset into two folds five times. For each of these five repetitions, the models are trained on one fold and tested on the other, and then the roles are reversed, creating two performance estimates per repetition [103]. The test statistic is calculated from these ten performance differences (two from each of the five repetitions) and follows a t-distribution [103]. This test is widely used due to its efficient use of data.

• Combined 5×2-fold Cross-Validation F-test: Proposed as an alternative to the paired t-test, this test uses the same 5×2 cross-validation setup but employs an F-statistic. Research has indicated that this test may have lower Type I error rates (reduced chance of falsely declaring a significant difference) compared to the paired t-test in certain scenarios, such as comparing survival models [103].

The diagram below illustrates the workflow for these resampling-based tests:

Tests for Comparing Multiple Models

When comparing more than two models simultaneously, different statistical approaches are necessary to control for the increased risk of false positives.

• Analysis of Variance (ANOVA) and Non-Parametric Alternatives: If the performance metrics are normally distributed, Repeated Measures ANOVA can test for significant differences between multiple models across the same data resamples. If the normality assumption is violated, the non-parametric Friedman Test should be used instead [105]. A significant result from these tests indicates that not all models perform equally but does not specify which pairs differ. For this, post-hoc tests (e.g., Nemenyi test) are required for pairwise comparisons [105].

The following table summarizes the selection criteria for key statistical tests based on data characteristics and comparison type.

Table 1: Guide to Selecting Statistical Tests for Model Comparison

Comparison Scenario	Data Characteristics	Recommended Test	Key Consideration
Two models	Paired performance metrics from resampling (e.g., CV)	5x2-fold CV Paired t-test [103]	Standard, efficient test for paired comparisons.
Two models	Paired performance metrics from resampling	Combined 5x2-fold CV F-test [103]	Preferred for potentially lower Type I error [103].
More than two models	Paired metrics, normally distributed	Repeated Measures ANOVA [105]	Requires normality assumption; must be followed by post-hoc tests.
More than two models	Paired metrics, distribution-free	Friedman Test [105]	Non-parametric alternative to Repeated Measures ANOVA.
Groups of models	Independent, non-normally distributed data	Kruskal-Wallis Test [105]	Non-parametric test for unpaired comparisons across >2 groups.

Practical Application: Experimental Protocols from Cancer Research

Protocol 1: Comparative Assessment of Osteosarcoma Classification Models

A study comparing eight machine learning algorithms for osteosarcoma cancer detection provides a robust example of statistical testing in practice [42].

• Data Preprocessing: The raw dataset was preprocessed using seven different combinations of denoising techniques (e.g., Principal Component Analysis, ANOVA) and data augmentation to create seven derived datasets.
• Model Training and Validation: Over 160 models were trained using eight ML algorithms on these datasets. Their hyperparameters were optimized via grid search.
• Performance Evaluation: Model performance was evaluated using repeated stratified 10-fold cross-validation. This robust validation technique provides multiple performance estimates for each model, creating the necessary data for statistical testing.
• Statistical Testing: The performance differences between the learned models were validated using a 5×2 cross-validation paired t-test to select the best-performing model with statistical confidence [42]. The final Extra Trees model achieved an AUC of 97.8% for osteosarcoma classification.

Protocol 2: Comparing Survival Models for Competing Risks

Research comparing the Fine-Gray (FG) model and the Random Survival Forest (RSF) for competing risks in survival analysis demonstrates how tests can reveal context-dependent superiority [103].

• Simulation Design: The study used low-dimensional simulated survival data with three different scenarios: linear relationships, quadratic relationships, and interactions between outcome and predictors.
• Model Training: Both FG and RSF models were trained on each simulated dataset to estimate the cumulative incidence function.
• Performance Measurement and Testing: The predictive performance was measured, and the 5×2-fold cv paired t-test and combined 5×2-fold cv F-test were applied to the results.
• Key Finding: The tests provided statistical evidence that the RSF model was superior in the presence of complex (quadratic) relationships, while the FG model was superior in linear simulations [103]. This highlights that the "best" model is often problem-dependent.

Implementing the described methodologies requires specific computational tools and resources. The following table details key solutions for building and comparing cancer classification models.

Table 2: Research Reagent Solutions for Model Development and Comparison

Tool / Resource	Type	Primary Function	Application Example
Python (with scikit-learn, SciPy)	Programming Library	Provides implementations of ML algorithms and statistical tests (e.g., t-tests, ANOVA) [18].	General-purpose environment for building the ML pipeline and performing statistical comparisons [18] [42].
R Statistical Software	Programming Environment	Offers comprehensive packages for survival analysis (e.g., survival) and advanced statistical testing.	Implementing Fine-Gray models, Random Survival Forests, and conducting specialized statistical tests [103] [104].
UCI Gene Expression Cancer RNA-Seq Dataset	Benchmark Data	Publicly available dataset with 801 samples and 20,531 genes across 5 cancer types [18].	Benchmarking pan-cancer classification algorithms and associated statistical tests [18].
The Cancer Genome Atlas (TCGA)	Data Repository	Comprehensive public database of genomic, transcriptomic, and clinical data for multiple cancer types.	Training and validating models on real-world, high-dimensional genomic data [18].
ISIC Dataset	Medical Image Data	Large public archive of dermoscopic images of skin lesions [60].	Benchmarking deep learning models for skin cancer classification from images [60].

Moving beyond single metric values to rigorous statistical testing is not merely a methodological refinement—it is a fundamental requirement for robust and reproducible cancer model research. As demonstrated, tests like the 5×2-fold cv paired t-test and the combined F-test provide a statistical framework to determine if observed performance differences are genuine [103]. The experimental protocols show that this rigor is achievable and necessary across diverse applications, from osteosarcoma detection [42] to survival analysis [103].

The consistent finding that model superiority is often context-dependent [103] [104] underscores the importance of this approach. By systematically applying the appropriate statistical tests detailed in this guide, researchers in oncology and drug development can make more reliable, data-driven decisions about which models truly hold promise for clinical translation.

In precision oncology, the transition of computational models from research tools to clinical assets hinges on a critical and quantifiable assessment: benchmarking against human expertise. Artificial intelligence (AI) and large language model (LLM) performance, no matter how impressive on internal datasets, only gains clinical relevance when its accuracy, consistency, and decision-making patterns are systematically compared to the gold standard of expert human judgment. This process establishes a crucial performance baseline, without which the real-world utility of a model remains uncertain. Such benchmarking is not merely about achieving a high accuracy score; it involves a nuanced analysis of where models excel, where they falter, and how their classification tendencies—such as overconfidence or excessive caution—align with clinical safety requirements. This guide objectively compares the performance of various AI models against human experts across key cancer diagnostic tasks, providing the experimental data and methodological context needed for researchers to critically evaluate and select models for development.

Performance Comparison Tables

Table 1: Benchmarking LLMs vs. Human Experts in Cancer Genetic Variant Classification

Model / Expert Group	Task Description	Accuracy	Key Performance Characteristics	Concordance with Human Experts
GPT-4o	Distinguishing clinically relevant variants from VUS (CIViC system)	73.18%	Conservative: correctly identified 94.1% of VUS but misclassified nearly half of clinically relevant variants as VUS [106].	Highest alignment with pathologist assessments [106].
Qwen 2.5	Same as above	57.31%	Tendency to overcall VUS as clinically relevant [106].	Lower agreement with both pathologists and reference standard [106].
Llama 3.1	Same as above	49.76%	Pronounced overclassification of VUS to clinically relevant categories [106].	Lowest agreement with human judgment [106].
Three-Model Consensus	Same as above	97.32%	In cases where all three LLMs agreed (26.3% of variants) [106].	Not explicitly measured but implied high concordance with ground truth.
Human Pathologists	Same as above	Not Specified	High inter-pathologist agreement, indicating strong consistency among experts [106].	Ground truth for model alignment assessment [106].

Table 2: Performance of AI Models in Cancer Diagnosis Categorization from EHR Data

Model	Data Type	Accuracy	Weighted Macro F1-Score	Performance Notes
GPT-4o	ICD Code Descriptions	90.8%	84.2	Matched BioBERT on ICD code accuracy [22].
BioBERT	ICD Code Descriptions	90.8%	84.2	Domain-specific model outperforming general LLMs on structured data [22].
GPT-4o	Free-Text Diagnoses	81.9%	71.8	Best performance on unstructured data [22].
BioBERT	Free-Text Diagnoses	81.6%	61.5	Performance drop on free-text compared to GPT-4o [22].
GPT-3.5, Gemini, Llama	Mixed	Lower Overall	Lower Overall	Consistently underperformed leading models on both data formats [22].

Table 3: Deep Learning Model Performance in Cancer Image Classification

Model / Study	Cancer Type	Task	Performance Metrics	Human Benchmark Comparison
DenseNet201 [15]	Breast Cancer	Benign vs. Malignant Classification	Accuracy: 89.4%, Precision: 88.2%, Recall: 84.1%, AUC: 95.8% [15].	Not directly compared to human experts in the provided data.
SkinEHDLF Hybrid Model [107]	Skin Cancer	Binary Classification (Melanoma vs. Benign)	Accuracy: 98.76%, AUROC: 99.8% [107].	Outperformed baseline models (ResNet-50, EfficientNet-B3, ViT-B16); clinical validation context implied [107].
YOLOv10 [108]	Blood Cell	Detection & Classification for Blood Cancers	Superior real-time performance and classification accuracy versus MobileNetV2, ShuffleNetV2 [108].	Aims to automate manual microscopy, reducing human effort and subjectivity [108].
AI Pathology Models (Review) [109]	Lung Cancer	Tumor Subtyping (e.g., LUAD vs. LUSC)	Average AUC values ranged from 0.746 to 0.999 across external validation studies [109].	Noted performance drop in external vs. internal validation; limited real-world clinical adoption due to validation gaps [109].

Experimental Protocols for Key Benchmarking Studies

Protocol 1: LLM Classification of Cancer Genetic Variants

The benchmarking study evaluating GPT-4o, Llama 3.1, and Qwen 2.5 established a rigorous protocol for assessing clinical reasoning capabilities in genetic variant interpretation [106].

• Data Source and Sample Size: The study utilized 10,506 genetic variants from Next-Generation Sequencing (NGS) testing reports of 612 patients who underwent FoundationOne CDx testing. The dataset included 5,240 clinically relevant variants and 5,266 Variants of Unknown Significance (VUS) [106].
• Ground Truth Definition: Ground truth annotations were derived directly from the FoundationOne CDx reports, where variants listed in the Genomic Findings section were designated "clinically relevant" and those in the Appendix as "VUS" [106].
• Task Formulation and Prompt Design: LLMs were instructed to classify genetic variants using the CIViC classification system (Levels A-E for clinically relevant, "VUS" otherwise) following a standardized system prompt (Supplementary Table 3 of the original study) [106].
• Human Expert Comparison: Three pathologists independently classified a randomly selected subset of 100 variants as clinically relevant or VUS, establishing a human performance benchmark and enabling assessment of inter-observer variability [106].
• Stability Analysis: To assess response consistency, models were queried 100 times per variant, with stability measured by the consistency ratio of identical responses across iterations [106].
• Performance Metrics: Primary metric was classification accuracy against FoundationOne annotations. Additional analyses included confusion matrices, distribution of misclassifications, and consensus accuracy when all three models agreed [106].

Protocol 2: External Validation of AI Pathology Models

A systematic scoping review of external validation studies for AI pathology models in lung cancer diagnosis established methodological standards for assessing clinical generalizability [109].

• Study Inclusion Criteria: The review included 22 studies that conducted external validation of AI models for lung cancer diagnosis from digital pathology images, defined as evaluation using data from a separate source than the training data [109].
• Model Tasks Assessment: Models were categorized by diagnostic task: classification of malignant vs. non-malignant tissue, tumor subtyping (e.g., LUAD vs. LUSC), tumor growth pattern classification, biomarker identification, and prediction of tumor cellularity [109].
• Methodological Rigor Evaluation: Studies were assessed using QUADAS-AI-P quality assessment tool across five domains: participant selection/study design, image selection, index test, reference standard, and flow and timing [109].
• Dataset Diversity Analysis: Technical and demographic diversity of external validation datasets were evaluated, including scanner variability, staining protocols, tissue preservation methods, and patient populations [109].
• Performance Metrics Extraction: Model performance metrics (particularly AUC values) were extracted and compared across studies, with note of performance degradation from internal to external validation [109].

Visualizing the Clinical Benchmarking Workflow

Clinical Benchmarking Workflow for Cancer AI Models

Table 4: Key Experimental Resources for Cancer AI Benchmarking Studies

Resource Category	Specific Examples	Function in Benchmarking Studies
Public Cancer Databases	OncoKB, CIViC, The Cancer Genome Atlas (TCGA), UCSC Genome Browser, Gene Expression Omnibus (GEO) [106] [110].	Provide structured, expert-curated genomic and clinical data for model training and validation against established biological evidence.
Clinical Datasets	FoundationOne CDx reports, Electronic Health Record (EHR) data with ICD codes and free-text diagnoses, Research Enterprise Data Warehouse [106] [22].	Serve as real-world ground truth for benchmarking model performance against clinical standards and expert annotations.
Specialized Language Models	BioBERT (bidirectional encoder representations from transformers for biomedical text) [22].	Domain-specific models pretrained on biomedical literature, providing baseline performance for biomedical NLP tasks.
General-Purpose LLMs	GPT-4o, GPT-3.5, Llama series, Gemini [106] [22].	General-purpose models evaluated for transfer learning capabilities in cancer domain tasks, benchmarked against specialized models.
Deep Learning Architectures	DenseNet201, ResNet50, VGG16, ConvNeXt, EfficientNetV2, Swin Transformer, YOLOv10 [15] [107] [108].	Core model architectures for image-based cancer classification, compared for accuracy, efficiency, and clinical applicability.
Pathology Imaging Platforms	Whole Slide Imaging (WSI) systems, Digital pathology repositories [109].	Enable digitization of pathology slides for AI analysis and facilitate external validation across multiple institutions.
Validation Frameworks	QUADAS-AI-P quality assessment tool, Statistical metrics (Accuracy, F1-Score, AUC), Bootstrapping for confidence intervals [22] [109].	Standardized methodologies for assessing model robustness, generalizability, and potential biases in clinical applications.

Analysis of Benchmarking Results and Clinical Implications

The collective evidence from these benchmarking studies reveals critical patterns in how computational models perform relative to human expertise across different cancer diagnostic domains. In genetic variant interpretation, GPT-4o demonstrated a notably conservative pattern, correctly identifying 94.1% of VUS but misclassifying nearly half of clinically relevant variants as VUS [106]. This contrasts with Llama 3.1 and Qwen 2.5, which showed tendencies toward overclassification of VUS as clinically relevant—a potentially riskier approach in clinical decision-making [106]. The high accuracy (97.32%) achieved when all three models agreed suggests potential for ensemble approaches to improve reliability [106].

For EHR data processing, the performance differential between structured ICD codes (90.8% accuracy for both GPT-4o and BioBERT) and free-text diagnoses (81.9% for GPT-4o) highlights the challenge of interpreting unstructured clinical language [22]. BioBERT's strong performance on structured data underscores the value of domain-specific training, while GPT-4o's advantage on free-text suggests better generalization to real-world clinical documentation patterns [22].

In medical imaging, the exceptional performance metrics of specialized deep learning models like DenseNet201 (95.8% AUC for breast cancer) and SkinEHDLF (99.8% AUROC for skin cancer) approach theoretical ceilings but must be interpreted with caution [15] [107]. The systematic review of lung cancer pathology models revealed that despite high AUC values (0.746-0.999), most studies used restricted datasets and retrospective case-control designs, with significant performance drops observed in external validation [109]. This underscores that impressive internal validation metrics do not necessarily translate to real-world clinical reliability.

These findings collectively suggest that while certain models approach or exceed human-level performance on specific, well-defined tasks, their clinical adoption requires careful consideration of their specific error patterns, consistency across diverse populations, and performance on external validation. The benchmarking methodologies outlined here provide researchers with the framework needed to make these critical assessments when selecting and implementing AI tools in precision oncology.

The integration of artificial intelligence (AI) and machine learning (ML) in oncology represents a paradigm shift in cancer diagnosis, prognosis, and treatment planning. These computational models promise to revolutionize clinical decision-making by extracting complex patterns from multidimensional data sources, including electronic health records (EHRs), medical images, and omics profiles [22] [17] [111]. However, the transition from experimental algorithms to clinically valuable tools hinges on a critical yet often underestimated process: rigorous validation. The path from internal to external validation separates mathematically interesting models from clinically useful tools, determining whether a model can generalize beyond the specific data on which it was developed to diverse patient populations and clinical settings [112] [111].

Internal validation, employing techniques such as cross-validation or bootstrap methods, provides an initial assessment of model performance on subsets of the development dataset [112] [113]. While this step is necessary for model refinement, it insufficiently predicts real-world performance. External validation evaluates model performance on completely independent datasets collected by different investigators from different institutions [112]. This distinction is not merely procedural but fundamental to clinical implementation. As the scoping review by [111] emphasizes, "the overwhelming majority of algorithms developed for cancer-related decisions have yet to reach oncology practice, mainly due to subpar methodological reporting and validation standards." This article systematically compares validation approaches across cancer classification models, providing researchers with a framework for assessing and demonstrating model generalizability.

Core Concepts: Internal Versus External Validation

Internal Validation Strategies

Internal validation techniques assess model performance using the available development data, providing crucial feedback during model building while helping to mitigate optimism bias [112] [113]. These methods aim to estimate how the model would perform on new data drawn from the same underlying population.

• K-fold Cross-Validation: The dataset is partitioned into k equally sized folds. The model is trained on k-1 folds and validated on the remaining fold, repeating this process k times with each fold serving once as the validation set. The performance estimates are then averaged across all iterations [113].
• Bootstrap Methods: Multiple samples are drawn with replacement from the original dataset to create training sets, with the out-of-bag instances used for validation. The conventional bootstrap can be over-optimistic, while the 0.632+ bootstrap method may be overly pessimistic, particularly with small sample sizes [113].
• Train-Test Split: The dataset is divided into a single training set and a held-out testing set, typically using a 70%-30% or 80%-20% split. While conceptually simple, this approach can yield unstable performance estimates, especially with smaller datasets [113].

Comparative simulation studies have demonstrated that k-fold cross-validation and nested cross-validation offer greater stability and reliability compared to train-test or bootstrap approaches, particularly when sample sizes are sufficient [113]. The choice of internal validation strategy becomes especially critical in high-dimensional settings, such as transcriptomic analysis, where the number of features (e.g., genes) vastly exceeds the number of observations [113].

External Validation as the Gold Standard

External validation represents a more rigorous procedure necessary for evaluating whether a predictive model will generalize to populations other than the one on which it was developed [112]. For an external dataset to provide a meaningful assessment of generalizability, it must be "truly external, that is, to play no role in model development and ideally be completely unavailable to the researchers building the model" [112].

The fundamental distinction between internal and external validation lies not merely in the data partitioning but in the conceptual objective: internal validation optimizes and provides preliminary performance estimates, while external validation tests the model's transportability across different clinical settings, patient demographics, and data collection protocols [112] [111]. This distinction is particularly crucial for models incorporating biomarkers, where inter-laboratory variation in assays and technological evolution of measurement platforms can significantly impact performance [112].

Performance Metrics Comparison Across Cancer Types

Quantitative Performance Benchmarks

Table 1: Performance Comparison of Externally Validated Cancer Classification Models

Cancer Type	Model Architecture	Data Modality	External Validation Performance	Key Metrics
Multiple Cancers (15 types) [114]	Multinomial Logistic Regression	Clinical factors, symptoms, blood tests	C-statistic: 0.876 (men), 0.844 (women) for any cancer	Discrimination, Calibration, Sensitivity, Net Benefit
Cervical Cancer [115]	ResNet50 (Deep Transfer Learning)	Pap smear images	Accuracy: 95% (2-class & 7-class) on Herlev dataset	Accuracy, Precision, Recall
Breast Cancer [116]	Multiple ML Algorithms	Clinical risk factors	No significant improvement over Gail model	Accuracy, Sensitivity, Precision
Cancer Diagnosis Categorization [22]	BioBERT	EHRs (ICD codes)	Weighted Macro F1-score: 84.2	F1-score, Accuracy
Cancer Diagnosis Categorization [22]	GPT-4o	EHRs (free-text)	Weighted Macro F1-score: 71.8	F1-score, Accuracy
Osteosarcoma [42]	Extra Trees Algorithm	Clinical dataset	AUROC: 97.8%, Prediction time: 10 ms	AUC, Inference Speed

Table 2: Internal vs. External Validation Performance Discrepancies

Study Context	Internal Validation Performance	External Validation Performance	Performance Gap	Primary Factors
High-dimensional prognosis models [113]	Unstable across methods (train-test, bootstrap)	Not applicable (simulation study)	Varies by method	Sample size, validation strategy
Breast cancer risk prediction [116]	AI algorithms showed promise during development	No significant improvement over traditional Gail model	Substantial	Limited feature set, dataset characteristics
Pan-cancer classification [17]	Up to 95.59% accuracy in original studies	Often lower in independent validations	Context-dependent	Tumor heterogeneity, technical variations

Analysis of Performance Patterns

The quantitative comparisons reveal several critical patterns in cancer classification model validation. First, model performance varies substantially across cancer types, with generally higher discrimination values observed in men compared to women in large-scale clinical prediction models [114]. Second, the complexity of the model architecture does not necessarily guarantee superior performance, as evidenced by the breast cancer risk prediction study where multiple AI algorithms failed to significantly outperform the traditional Gail model [116]. Third, the data modality significantly influences achievable performance levels, with image-based models generally demonstrating higher accuracy compared to those utilizing structured EHR data or clinical risk factors alone [115] [116].

The performance gaps observed between internal and external validation highlight the critical importance of rigorous validation practices. As noted in [112], "models involving biomarkers require careful validation for two reasons: issues with overfitting when complex models involve a large number of biomarkers, and inter-laboratory variation in assays used to measure biomarkers." These factors become particularly pronounced in external validation settings, where technical variations and population differences introduce additional heterogeneity not captured during internal validation.

Experimental Protocols for Validation Studies

Large-Scale Clinical Prediction Model Development

The recent Nature Communications study [114] provides a comprehensive protocol for developing and externally validating cancer prediction algorithms:

• Derivation Cohort: Utilized 7.46 million adults from English primary care data (QResearch database), including 129,715 incident cancers, with predictors including age, sex, deprivation, smoking, alcohol, family history, medical diagnoses, symptoms, and blood tests (full blood count and liver function tests).
• Model Development: Employed multinomial logistic regression to develop separate equations for men and women predicting the absolute probability of 15 cancer types. Two final models were derived: Model A (clinical factors and symptoms) and Model B (additionally including blood test results).
• External Validation: Performed in two separate cohorts - 2.64 million patients in England (QResearch validation cohort) and 2.74 million from Scotland, Wales and Northern Ireland (CPRD validation cohort). Discrimination was assessed using c-statistics, calibration through calibration plots, and clinical utility with decision curve analysis.
• Handling of Missing Data: Implemented multiple imputation for missing predictor values, with sensitivity analyses conducted to assess the potential impact of missing data.

This protocol exemplifies comprehensive external validation across diverse populations, a key strength highlighted by the authors [114]. The inclusion of blood tests as affordable digital biomarkers represents an innovation that improved performance compared to existing models.

Cross-Institutional ML Model Validation

The osteosarcoma detection study [42] demonstrates a rigorous methodology for comparing machine learning algorithms:

• Data Preprocessing: Explored a publicly available raw osteosarcoma dataset, applying different combinations of data denoising techniques (principal component analysis, mutual information gain, analysis of variance, Kendall's rank correlation) and data augmentation to derive seven different datasets.
• Model Training and Comparison: Designed and performed extensive comparative analysis of seven sets of ML models (altogether over 160 models) using eight ML algorithms with hyperparameters optimized via grid search.
• Validation Approach: Employed repeated stratified 10-fold cross-validation and 5×2 cross-validation paired t-tests to select the best model. The extra trees algorithm demonstrated superior performance, achieving 97.8% AUROC with a prediction time of 10 ms.
• Class Imbalance Handling: Addressed through random oversampling and multicollinearity removal via principal component analysis.

This systematic approach to comparing multiple algorithms on derived datasets provides a robust framework for algorithm selection in cancer classification tasks [42].

Internal Validation Simulation Design

The simulation study focusing on high-dimensional prognosis models [113] offers specific guidance for internal validation strategies:

• Simulation Framework: Conducted using data from the SCANDARE head and neck cohort (n = 76 patients). Simulated datasets included clinical variables and transcriptomic data (15,000 transcripts) with disease-free survival endpoints.
• Sample Size Considerations: Evaluated sample sizes of 50, 75, 100, 500, and 1000 patients, with 100 replicates for each scenario to assess stability across different data constraints.
• Validation Comparison: Compared train-test (70% training), bootstrap (100 iterations), 5-fold cross-validation, and nested cross-validation (5×5) to assess discriminative (time-dependent AUC and C-Index) and calibration (3-year integrated Brier Score) performance.
• Model Selection: Utilized Cox penalized regression for model selection in high-dimensional settings where the number of features greatly exceeds the number of observations.

This study specifically recommended k-fold cross-validation and nested cross-validation for internal validation of Cox penalized models in high-dimensional time-to-event settings, noting their superior stability and reliability compared to train-test or bootstrap approaches [113].

Visualization of Validation Workflows and Relationships

Cancer Model Validation Pathway

Cancer Model Validation Pathway - This diagram illustrates the sequential progression from model development through internal validation, external validation, clinical utility assessment, and eventual deployment, highlighting critical decision points.

Performance Metrics Relationships

Performance Metrics Relationships - This diagram categorizes the essential metrics for evaluating cancer classification models, emphasizing the need to assess discrimination, calibration, and clinical utility beyond simple accuracy.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagent Solutions for Cancer Model Validation

Tool/Category	Specific Examples	Function in Validation	Implementation Considerations
Statistical Software	Python (Scikit-learn), R	Implementation of validation algorithms, performance metric calculation	Ensure version control, reproducible environments
Validation Frameworks	K-fold CV, Bootstrap, Nested CV	Internal validation performance estimation	Select based on sample size and data structure [113]
Performance Metrics	C-statistic, Brier Score, Calibration Plots	Comprehensive model assessment	Report multiple metrics for different aspects of performance [112] [114]
Biomedical Databases	TCGA Pan-Cancer Atlas, GEO, UCSC Genome Browser	Source of multi-omics data for development and validation	Address heterogeneity and technical batch effects [17]
Clinical Data Repositories	QResearch, CPRD, Research Enterprise Data Warehouse	Large-scale electronic health records for validation	Ensure data quality, completeness assessment [114] [22]
Deep Learning Frameworks	TensorFlow, PyTorch, Keras	Implementation of CNN, ResNet, other architectures	Computational resource requirements, transfer learning options [115] [117]
Model Interpretation Tools	SHAP, LIME, Guided Grad-CAM	Feature importance analysis, model transparency	Critical for clinical adoption and trust [17] [115]

The systematic comparison of validation approaches across cancer classification models reveals a critical consensus: external validation remains the indispensable benchmark for assessing true model generalizability and readiness for clinical implementation. While internal validation strategies continue to evolve, particularly for high-dimensional settings [113], they consistently overestimate real-world performance compared to external validation [112] [111]. The successful integration of machine learning in oncology decision-making necessitates standardized data methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessments [111].

Future directions must address persistent challenges in cancer model validation, including limited international validation across diverse ethnicities, inconsistent data sharing practices, disparities in validation metrics reporting, and insufficient calibration documentation [111]. Furthermore, as cancer models increasingly incorporate multi-omics data [17] and complex deep learning architectures [115] [117], validation frameworks must adapt to these technological advancements while maintaining rigorous assessment standards. Only through comprehensive validation pathways that progress from internal to external assessment can researchers transform promising algorithms into clinically valuable tools that genuinely improve cancer patient care.

The integration of artificial intelligence (AI) into oncology is transforming cancer care, from enhancing diagnostic accuracy to personalizing treatment strategies. This guide provides an objective comparison of the performance of various AI models, including large language models (LLMs), convolutional neural networks (CNNs), and other deep learning architectures, in oncology-specific tasks. Framed within the broader context of performance metrics for cancer classification model research, this analysis synthesizes findings from recent studies to offer insights for researchers, scientists, and drug development professionals. We focus on quantitative performance data, detailed experimental methodologies, and the essential tools required to implement these technologies effectively.

At a Glance: Performance of AI Models in Key Oncological Tasks

The following tables summarize the performance of various AI models across critical oncology applications, including diagnostic classification, information extraction, and cancer progression prediction.

Table 1: Performance of AI Models in Cancer Diagnosis Classification from EHR Data (Based on [22])

Model Name	Model Type	Task Description	Data Format	Accuracy (%)	Weighted Macro F1-Score
BioBERT	Domain-specific LLM	Categorizing diagnoses into 14 cancer types	ICD Code Descriptions	90.8	84.2
GPT-4o	General-purpose LLM	Categorizing diagnoses into 14 cancer types	ICD Code Descriptions	90.8	Not Specified
GPT-4o	General-purpose LLM	Categorizing diagnoses into 14 cancer types	Free-Text Entries	81.9	71.8
BioBERT	Domain-specific LLM	Categorizing diagnoses into 14 cancer types	Free-Text Entries	81.6	61.5
GPT-3.5, Gemini, Llama	General-purpose LLMs	Categorizing diagnoses into 14 cancer types	ICD & Free-Text	Lower Overall	Lower Overall

Table 2: Performance of Specialized vs. General AI Models in Oncology-Specific Tasks

Model Name	Model Type	Task Description	Key Performance Metric	Result	Reference Dataset
Woollie (65B)	Oncology-specific LLM	Predicting cancer progression	AUROC (Overall)	0.97	MSK (39,319 notes)
Woollie (65B)	Oncology-specific LLM	Predicting pancreatic cancer progression	AUROC	0.98	MSK
Woollie (65B)	Oncology-specific LLM	External validation on lung cancer detection	AUROC	0.95	UCSF (600 notes)
OvCan-FIND	Specialized Deep Learning Model	Classifying ovarian cancer from histopathology images	Accuracy	99.74%	Ovarian Cancer Image Dataset
Enhanced Deep Learning Model (DenseNet121)	Specialized Deep Learning Model	Classifying breast cancer from histopathology images	Binary Classification Accuracy	97.1%	BreaKHis Dataset

Table 3: AI Diagnostic Performance in Cancer Imaging (Umbrella Review of 158 Studies [118])

Cancer Type	Sensitivity Range (%)	Specificity Range (%)	Noteworthy Performance
Esophageal Cancer	90 - 95	80 - 93.8	High, consistent performance
Breast Cancer	75.4 - 92	83 - 90.6	Good specificity
Ovarian Cancer	75 - 94	75 - 94	Balanced sensitivity & specificity
Lung Cancer	Not Specified	65 - 80	Relatively low specificity

Detailed Experimental Protocols and Model Methodologies

NLP for Cancer Diagnosis Categorization from EHR

Objective: To evaluate the performance of four LLMs (GPT-3.5, GPT-4o, Llama 3.2, Gemini 1.5) and BioBERT in classifying cancer diagnoses from both structured (ICD code descriptions) and unstructured (free-text) data in Electronic Health Records into 14 predefined, clinically relevant categories [22].

Dataset:

• Source: Research Enterprise Data Warehouse, University of Tennessee Medical Center (2017-2021) [22].
• Composition: 3,456 patient records, containing 762 unique diagnoses (326 ICD code descriptions and 436 free-text entries) [22].
• Predefined Categories: Benign, Breast, Lung/Thoracic, Prostate, Gynecologic, Head/Neck, Gastrointestinal, Central Nervous System, Metastasis, Skin, Soft Tissue, Hematologic, Genitourinary, and Unknown [22].

Model Implementation:

• GPT-3.5 & GPT-4o (OpenAI): Accessed via cloud API [22].
• Llama 3.2 (Meta): 70B parameter version implemented locally using Ollama with 4-bit quantization [22].
• Gemini 1.5 (Google): Accessed via Google Cloud Vertex AI, temperature set to 1 [22].
• BioBERT (dmis-lab): The dmis-lab/biobert-base-cased-v1 model from Hugging Face, trained for 3 epochs [22].

Prompt Design and Validation:

• A standardized prompt was used for LLMs: "Given the following ICD-10 description or treatment note for a radiation therapy patient: {input}, select the most appropriate category from the predefined list: {Category list}. Respond only with the exact category name from the list..." [22].
• Outputs were cleaned and standardized using a Python function leveraging the difflib library for string similarity matching [22].
• Two oncology experts independently validated the model classifications to establish ground truth and calculate accuracy and agreement rates [22].

Performance Metrics:

• Primary Metrics: Accuracy and weighted macro F1-score [22].
• Statistical Analysis: 95% confidence intervals were computed using nonparametric bootstrapping to quantify uncertainty around performance estimates [22].

Diagram 1: Experimental workflow for classifying cancer diagnoses from EHR data using multiple AI models [22].

Development and Validation of an Oncology-Specific LLM (Woollie)

Objective: To develop and validate Woollie, an open-source LLM specifically designed for oncology, and evaluate its performance against general-purpose models like ChatGPT in predicting cancer progression from radiology reports [119].

Model Development:

• Base Architecture: Built upon Meta's open-source Llama models (Llama 1) [119].
• Training Data: Trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers [119].
• Training Strategy: Employed a "stacked alignment" process to incrementally build domain knowledge, starting with a base Llama model, progressing to Woollie Foundation, then Woollie Medicine, and finally the specialized Woollie model. This approach mitigated catastrophic forgetting [119].
• Model Variants: Four model sizes were developed: 7B, 13B, 33B, and 65B parameters [119].

Validation and Evaluation:

• Internal Validation: Analyzed 39,319 radiology impression notes from 4,002 patients from MSK [119].
• External Validation: Used an independent dataset of 600 radiology impressions from 600 unique patients from the University of California, San Francisco (UCSF) [119].
• Benchmarking: Performance was assessed on standard medical benchmarks (PubMedQA, MedMCQA, USMLE) and non-medical benchmarks, comparing against base Llama models and GPT-4 [119].
• Primary Task: Predicting cancer progression from radiology impression notes, with performance measured by Area Under the Receiver Operating Characteristic Curve (AUROC) [119].

Deep Learning for Histopathological Image Classification

Objective: To develop and validate highly accurate deep learning models for classifying cancer subtypes from histopathological images, as exemplified by studies on ovarian and breast cancer [120] [7].

Ovarian Cancer Classification (OvCan-FIND Model):

• Dataset: An ovarian cancer image dataset containing samples labeled as Clear Cell, Endometri, Mucinous, Serous, and Non-Cancerous [120].
• Model Comparison: The proposed OvCan-FIND model was compared against state-of-the-art CNN architectures like Inception V3, EfficientNet variants, ResNet152V2, MobileNet, VGG16, VGG19, and Xception [120].
• Performance Metric: The primary metric was classification accuracy, with the OvCan-FIND model achieving 99.74% [120].

Breast Cancer Classification:

• Dataset: The publicly available BreaKHis dataset of histopathological biopsy images [7].
• Model Architecture: A deep learning model based on a DenseNet121 backbone, incorporating a multi-scale feature fusion strategy [7].
• Validation: 5-fold cross-validation was used to ensure robust performance estimation [7].
• Performance Metrics: Binary classification accuracy (97.1%), and subtype classification accuracies for benign (93.8%) and malignant (92.0%) tumors [7].

The Scientist's Toolkit: Essential Research Reagents and Materials

This table details key resources and their functions as employed in the featured experiments, providing a practical guide for replicating or building upon this research.

Table 4: Essential Research Reagents and Computational Tools for AI in Oncology

Item Name	Type/Category	Primary Function in Research	Example Source/Reference
Research Enterprise Data Warehouse	Data Resource	Provides real-world, de-identified EHR data for training and testing NLP models.	University of Tennessee [22]
MSK Radiology Reports	Data Resource	A large, curated dataset of oncology-specific radiology notes for training specialized LLMs.	Memorial Sloan Kettering Cancer Center [119]
BreaKHis Dataset	Data Resource	A public benchmark dataset of histopathological breast cancer images for model training and validation.	[7]
Ovarian Cancer Image Dataset	Data Resource	A curated dataset of ovarian histopathology images across multiple subtypes for classification tasks.	[120]
BioBERT (dmis-lab/biobert-base-cased-v1)	Software/Model	A domain-specific BERT model pre-trained on biomedical literature, used as a baseline or for fine-tuning.	Hugging Face [22]
Llama Models (Meta)	Software/Model	Open-source foundation LLMs that serve as the base architecture for building specialized models like Woollie.	Meta [119]
Ollama	Software/Tool	Enables local deployment and management of LLMs like Llama, addressing data privacy concerns.	[22]
Google Cloud Vertex AI	Software/Platform	A managed machine learning platform used to configure and run models like Gemini 1.5.	Google [22]
DenseNet121	Software/Model	A CNN backbone architecture known for its efficiency, used in histopathological image classification models.	[7]
Joanna Briggs Institute (JBI) Checklist	Methodology Tool	A critical appraisal tool for assessing the methodological quality of systematic reviews.	[118]

Diagram 2: A layered overview of the core components in an AI-driven oncology research stack, showing the relationship between data, models, and applications [22] [119] [120].

This comparative analysis underscores a clear trend in AI for oncology: domain-specific models consistently outperform their general-purpose counterparts in specialized clinical tasks. BioBERT and Woollie demonstrated superior capabilities in processing biomedical text and predicting cancer progression, respectively, while specialized deep learning models like OvCan-FIND achieved exceptional accuracy in histopathological image classification. However, general-purpose LLMs remain highly effective for tasks like patient-friendly guideline dissemination, with models like DeepSeek showing notable regional adaptability [121].

The successful application of these models hinges on robust experimental protocols, including expert validation, cross-institutional testing, and the use of standardized performance metrics. As the field evolves, addressing challenges such as model generalizability, transparency (XAI), and seamless integration into clinical workflows will be paramount. The tools and methodologies outlined in this guide provide a foundation for researchers and drug development professionals to critically evaluate and implement AI solutions that can ultimately advance personalized cancer care and improve patient outcomes.

Conclusion

Selecting the right performance metrics is not a one-size-fits-all process but a critical, context-dependent decision in cancer model development. A thorough understanding of foundational metrics, combined with strategic application and rigorous validation, is essential for translating algorithmic performance into clinically meaningful insights. The future of cancer classification lies in AI-driven, multimodal approaches that integrate histopathology, genomics, and radiomics. For these advanced models, establishing standardized metric reporting and validation frameworks will be paramount. Ultimately, the choice of metrics must be guided by the clinical question at hand, whether it demands maximizing recall to avoid missing a single cancer case or optimizing precision to prevent unnecessary patient anxiety and procedures, thereby ensuring these powerful tools reliably advance the field of precision oncology.

References

• 1. Confusion matrix [https://en.wikipedia.org/wiki/Confusion_matrix]
• 2. Demystifying the Confusion Matrix: A Short Guide [https://www.lightly.ai/blog/confusion-matrix]
• 3. Understanding the Confusion Matrix in Machine Learning [https://www.geeksforgeeks.org/machine-learning/confusion-matrix-machine-learning/]
• 4. How to Visualize a Binary Classification Confusion Matrix [https://www.linkedin.com/advice/1/what-best-practices-visualizing-binary-classification-ourrc]
• 5. Classification: Accuracy, recall, precision, and related metrics [https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall]
• 6. Sensitivity ,Specificity, and Accuracy [https://www.analyticsvidhya.com/blog/2021/06/classification-problem-relation-between-sensitivity-specificity-and-accuracy/]
• 7. Frontiers | An artificial intelligence model for early-stage breast cancer ... [https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1627876/full]
• 8. JMIR Bioinformatics and Biotechnology - Stacked Deep Learning... [https://bioinform.jmir.org/2025/1/e70709]
• 9. Performance Metrics: Confusion matrix, Precision, Recall ... [https://towardsdatascience.com/performance-metrics-confusion-matrix-precision-recall-and-f1-score-a8fe076a2262/]
• 10. Evaluating Classification Models Using Accuracy, Precision ... [https://frankcorso.me/classification-machine-learning-metrics]
• 11. ML model evaluation metrics (Accuracy, Precision, Recall, ... [https://medium.com/@data.science.enthusiast/ml-model-evaluation-metrics-accuracy-precision-recall-specificity-f1-score-and-negative-9f3f9d103173]
• 12. Precision and recall [https://en.wikipedia.org/wiki/Precision_and_recall]
• 13. A Hybrid Deep Learning Ensemble for Accurate Skin ... [https://www.medrxiv.org/content/10.1101/2025.09.03.25335044v1.full-text]
• 14. F1 Score in Machine Learning Explained [https://encord.com/blog/f1-score-in-machine-learning/]
• 15. Breast Cancer Classification with Various Optimized Deep ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12293705/]
• 16. Predicting cancer risk using machine learning on lifestyle ... [https://www.nature.com/articles/s41598-025-15656-8]
• 17. Computational models for pan-cancer classification based on ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12599994/]
• 18. Machine learning approach to identify significant genes ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12547461/]
• 19. Cancer Multi-Omics Database for Machine Learning [https://www.nature.com/articles/s41597-025-05235-x]
• 20. Pan-cancer, multi-omic correlates of survival transcending ... [https://www.nature.com/articles/s41698-025-01029-x]
• 21. Multi-Omics Data Integration for Improved Cancer ... [https://pubmed.ncbi.nlm.nih.gov/41300698/]
• 22. Model Performance Evaluation Study - JMIR Cancer [https://cancer.jmir.org/2025/1/e72005]
• 23. Pan-cancer multi-omics analysis reveals IQCE as a ... [https://link.springer.com/article/10.1007/s12672-025-03841-0]
• 24. Accuracy vs. F -Score. A comparison between Accuracy and | Medium 1 [https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2]
• 25. Mastering the F1 Score: A Practical Guide for Machine ... [https://www.lightly.ai/blog/f1-score]
• 26. A multi-representation deep-learning framework for accurate ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12628999/]
• 27. Cross-platform multi-cancer histopathology classification ... [https://www.nature.com/articles/s41598-025-24791-1]
• 28. Enhanced skin cancer diagnosis: a deep feature extraction ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11601079/]
• 29. Current composite-feature classification methods do not ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2013.00289/full]
• 30. Identification and transfer of spatial transcriptomics signatures for... [https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-019-1242-9]
• 31. Composite Grading Algorithm for the National Cancer ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7878323/]
• 32. Cancer Diagnosis Categorization in Electronic Health ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12490771/]
• 33. Accuracy, precision, and recall in multi-class classification [https://www.evidentlyai.com/classification-metrics/multi-class-metrics]
• 34. Micro Average vs Macro average Performance in a ... [https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin]
• 35. Micro, Macro & Weighted Averages of F1 Score, Clearly ... [https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f/]
• 36. Micro metrics vs macro metrics [https://stackoverflow.com/questions/68708610/micro-metrics-vs-macro-metrics]
• 37. Imbalanced Classification [https://towardsai.net/p/data-science/imbalanced-classification]
• 38. Learning from Imbalanced Data [https://pmc.ncbi.nlm.nih.gov/articles/PMC11476323/]
• 39. Handling imbalanced medical datasets: review of a decade ... [https://link.springer.com/article/10.1007/s10462-024-10884-2]
• 40. Evaluation Metrics in Machine Learning [https://www.geeksforgeeks.org/machine-learning/metrics-for-machine-learning-model/]
• 41. F1 Score vs ROC AUC vs Accuracy vs PR AUC [https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc]
• 42. A comparative assessment of machine learning models ... [https://www.sciencedirect.com/science/article/pii/S2772442524000820]
• 43. Cancer Diagnosis through Contour Visualization of Gene ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10670706/]
• 44. Classification: ROC and AUC | Machine Learning | Google for Developers [https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc]
• 45. Receiver operating characteristic [https://en.wikipedia.org/wiki/Receiver_operating_characteristic]
• 46. The clinical meaning of the area under a receiver operating ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10089712/]
• 47. AUC ROC Curve in Machine Learning [https://www.geeksforgeeks.org/machine-learning/auc-roc-curve/]
• 48. How to explain the ROC AUC score and ROC curve? [https://www.evidentlyai.com/classification-metrics/explain-roc-curve]
• 49. ROC AUC Explained: A Beginner's Guide to Evaluating ... [https://towardsdatascience.com/roc-auc-explained-a-beginners-guide-to-evaluating-classification-models/]
• 50. The Diagnostic Accuracy of an Abbreviated vs. a Full MRI ... [https://pubmed.ncbi.nlm.nih.gov/40564819/]
• 51. The Diagnostic Accuracy of an Abbreviated vs. a Full MRI ... [https://www.mdpi.com/2075-4418/15/12/1497]
• 52. A review of machine learning methods for cancer ... [https://www.nature.com/articles/s41698-024-00617-7]
• 53. Use of the Area Under the Precision-Recall Curve to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12133047/]
• 54. Understanding Precision vs. Recall in Model Evaluation [https://viso.ai/computer-vision/precision-recall/]
• 55. PR曲线与ROC曲线：分类模型评估的双璧 [https://blog.csdn.net/2401_86968005/article/details/148387521]
• 56. The receiver operating characteristic curve accurately ... [https://www.sciencedirect.com/science/article/pii/S2666389924001090]
• 57. ROC AUC vs Precision-Recall for Imbalanced Data [https://machinelearningmastery.com/roc-auc-vs-precision-recall-for-imbalanced-data/]
• 58. A Comparison of Techniques for Class Imbalance in Deep ... [https://www.mdpi.com/2075-4418/13/1/67]
• 59. current advances and challenges - PMC - PubMed Central - NIH [https://pmc.ncbi.nlm.nih.gov/articles/PMC12568748/]
• 60. Enhanced skin cancer classification using modified ... [https://www.nature.com/articles/s41598-025-22228-3]
• 61. Deep learning algorithm for pathological grading of renal cell ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12329742/]
• 62. Advanced skin cancer prediction with medical image data ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12332096/]
• 63. Enhanced melanoma and non-melanoma skin cancer ... [https://www.nature.com/articles/s41598-025-08954-8]
• 64. Skin cancer classification based on a hybrid deep model ... [https://www.sciencedirect.com/science/article/abs/pii/S1746809424011674]
• 65. Pan-cancer classification by regularized multi-task learning [https://www.nature.com/articles/s41598-021-03554-8]
• 66. Pan-cancer image-based detection of clinically actionable ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7610412/]
• 67. A comparative study on breast cancer classification with ... [https://www.sciencedirect.com/science/article/pii/S1687850724002644]
• 68. Mean Average Precision (mAP) Explained: Everything You Need to... [https://www.v7labs.com/blog/mean-average-precision]
• 69. Choosing the Right Metrics: Recall, Precision, PR Curve ... [https://medium.com/@juanc.olamendy/choosing-the-right-metrics-recall-precision-pr-curve-and-roc-curve-explained-682259961cbe]
• 70. How to use classification threshold to balance precision and recall [https://www.evidentlyai.com/classification-metrics/classification-threshold]
• 71. Evaluating certainty of AI models for brain tumour detection [https://www.sciencedirect.com/science/article/abs/pii/S0010482525007267]
• 72. Large language models enable tumor-type classification ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12490231/]
• 73. DSSCC net enhanced skin cancer classification using ... [https://www.nature.com/articles/s41598-025-25428-z]
• 74. Refining cancer prediction with DNA sequencing and ... [https://www.nature.com/articles/s41598-025-23359-3]
• 75. The Precision - Recall - Trade . By George Bennett | Medium Off [https://datascience-george.medium.com/the-precision-recall-trade-off-aa295faba140]
• 76. Cancer classification using machine learning and HRV ... [https://www.nature.com/articles/s41598-021-01779-1]
• 77. An Enhancement in Cancer Classification Accuracy Using ... [https://www.mdpi.com/2073-8994/12/2/271]
• 78. Estimating cancer screening sensitivity and specificity ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9484579/]
• 79. A comprehensive data level analysis for cancer diagnosis ... [https://www.sciencedirect.com/science/article/pii/S1532046418302302]
• 80. The advantages of the Matthews correlation coefficient (MCC ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7]
• 81. Why Matthews Correlation Coefficient (MCC) Should Be ... [https://medium.com/@Hiadore/why-matthews-correlation-coefficient-mcc-should-be-your-go-to-metric-for-binary-classification-94a2e9941f3b]
• 82. Understanding the Matthews Correlation Coefficient (MCC) in ... [https://koshurai.medium.com/understanding-the-matthews-correlation-coefficient-mcc-in-machine-learning-26e8049f8572]
• 83. The Matthews correlation coefficient (MCC) is more reliable ... [https://biodatamining.biomedcentral.com/articles/10.1186/s13040-021-00244-z]
• 84. The Matthews correlation coefficient (MCC) is more reliable ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7863449/]
• 85. Performance Comparison of Balanced and Unbalanced ... [https://ui.adsabs.harvard.edu/abs/arXiv:2012.05585]
• 86. Integrating radiomics, artificial intelligence, and molecular ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12399666/]
• 87. Radiomics in Brain Tumor: Image Assessment, Quantitative ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5812810/]
• 88. Systematic review and meta-analysis on the classification ... [https://www.sciencedirect.com/science/article/pii/S2405844024123444]
• 89. Genomic pan-cancer classification using image-based ... [https://www.sciencedirect.com/science/article/pii/S2001037021000143]
• 90. Joint modeling histology and molecular markers for cancer ... [https://www.sciencedirect.com/science/article/pii/S1361841525000532]
• 91. A comprehensive genomic pan-cancer classification using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5496318/]
• 92. A Multi-Stage Approach to Breast Cancer Classification Using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9818545/]
• 93. Best holdout assessment is sufficient for cancer ... [https://www.sciencedirect.com/science/article/pii/S2666389924002873]
• 94. Implementation of ensemble machine learning algorithms on ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05050-w]
• 95. A Guide to Cross-Validation for Artificial Intelligence in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10388213/]
• 96. Cross-Validation Techniques: Evaluate Your ML Model ... [https://www.analyticsvidhya.com/blog/2021/05/4-ways-to-evaluate-your-machine-learning-model-cross-validation-techniques-with-python-code/]
• 97. Cross validation – a safeguard for machine learning models [https://ardigen.com/cross-validation-a-safeguard-for-machine-learning-models/]
• 98. Cross-validation (statistics) [https://en.wikipedia.org/wiki/Cross-validation_(statistics)]
• 99. A Gentle Introduction to k-fold Cross-Validation [https://www.machinelearningmastery.com/k-fold-cross-validation/]
• 100. Practical Considerations and Applied Examples of Cross ... [https://ai.jmir.org/2023/1/e49023]
• 101. Cross-Validation Visualized: A Narrative Guide to ... [https://www.mdpi.com/2504-4990/6/2/65]
• 102. Evaluation metrics and statistical tests for machine learning [https://www.nature.com/articles/s41598-024-56706-x]
• 103. Statistical approaches to identifying significant differences in ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279435]
• 104. Comparison of machine learning methods versus traditional ... [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-025-02694-z]
• 105. Choosing the Correct Statistical Test - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8327789/]
• 106. Benchmarking large language models GPT-4o, llama 3.1, ... [https://www.nature.com/articles/s41698-025-00935-4]
• 107. SkinEHDLF a hybrid deep learning approach for accurate ... [https://www.nature.com/articles/s41598-025-98205-7]
• 108. Advancing blood cell detection and classification [https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-03027-2]
• 109. Systematic scoping review of external validation studies ... [https://www.nature.com/articles/s41698-025-00940-7]
• 110. Computational models for pan-cancer classification based ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1667325/full]
• 111. Externally validated and clinically useful machine learning ... [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-025-02463-y]
• 112. Validation of Biomarker-based risk prediction models - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3896456/]
• 113. Internal validation strategy for high dimensional prognosis ... [https://www.sciencedirect.com/science/article/pii/S2001037025003563]
• 114. Development and external validation of prediction ... [https://www.nature.com/articles/s41467-025-57990-5]
• 115. Comparison of deep transfer learning models for ... [https://www.nature.com/articles/s41598-024-74531-0]
• 116. Comparison of Machine Learning Models for Classification ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11965882/]
• 117. An automatic classification of breast cancer using fuzzy ... [https://www.nature.com/articles/s41598-025-07013-6]
• 118. Artificial Intelligence Performance in Image-Based Cancer ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12000792/]
• 119. Large language model trained on clinical oncology data ... [https://www.nature.com/articles/s41746-025-01780-2]
• 120. An enhanced deep learning model for accurate ... [https://www.nature.com/articles/s41598-025-07903-9]
• 121. Comparative analysis of artificial intelligence tools for the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12626496/]