Machine learning helps identifying volume-confounding effects in radiomics

Alberto Traverso*, Michal Kazmierski*, Ivan Zhovannik, Mattea Welch, Leonard Wee, David Jaffray, Andre Dekker, Andrew Hope

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Web of Science)


Purpose: Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features.


841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness.


Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression.


The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development.

Original languageEnglish
Pages (from-to)24-30
Number of pages7
JournalPhysica Medica: European journal of medical physics
Publication statusPublished - Mar 2020
EventInternational Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) - Montreal, Canada
Duration: 17 Jun 201921 Jun 2019


  • Radiomics
  • Machine learning
  • Predictions
  • Lung
  • Head and neck

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