ArticleAcademic radiology2024
Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines.
Article in Academic radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automated Radiomics Model for Preoperative Pancreatic Neuroendocrine Tumor Grade Prediction.Annals of surgical oncology · 2026Article
- Robustness Analyses Substantially Narrow in silico Network-Toxicology Signals Linking Selected Pharmaceuticals and Caffeine to Vitiligo.Clinical, cosmetic and investigational dermatology · 2026Article
- Endophenotype Research in Epilepsy Across Time.Brain sciences · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Radiomics incorporating deep features for predicting Parkinson's disease inEJNMMI physics · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
RATIONALE AND
objectivesThis study demonstrates a method for quantifying the impact of overfitting on the receiving operator characteristic curve (AUC) when using standard analysis pipelines to develop imaging biomarkers. We illustrate the approach using two publicly available repositories of radiology and pathology images for breast cancer diagnosis. MATERIALS AND
methodsFor each dataset, we permuted the outcome (cancer diagnosis) values to eliminate any true association between imaging features and outcome. Seven types of classification models (logistic regression, linear discriminant analysis, Naïve Bayes, linear support vector machines, nonlinear support vector machine, random forest, and multi-layer perceptron) were fitted to each scrambled dataset and evaluated by each of four techniques (all data, hold-out, 10-fold cross-validation, and bootstrapping). After repeating this process for a total of 50 outcome permutations, we averaged the resulting AUCs. Any increase over a null AUC of 0.5 can be attributed to overfitting.
resultsApplying this approach and varying sample size and the number of imaging features, we found that failing to control for overfitting could result in near-perfect prediction (AUC near 1.0). Cross-validation offered greater protection against overfitting than the other evaluation techniques, and for most classification algorithms a sample size of at least 200 was required to assess as few as 10 features with less than 0.05 AUC inflation attributable to overfitting.
conclusionThis approach could be applied to any curated dataset to suggest the number of features and analysis approaches to limit overfitting.
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