ArticleScientific reports2024
The effect of data resampling methods in radiomics.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.Scientific reports · 2026Article
- An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- mpMRI-based MGMT methylation status prediction for glioblastoma through off-the-shelf deep features: A multi-dataset feasibility study.Journal of applied clinical medical physics · 2025Article
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
- RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability.Diagnostics (Basel, Switzerland) · 2025Article
- Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.EJNMMI physics · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- (KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network.Frontiers in big data · 2025Article
- Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma.BJR artificial intelligence · 2025Article
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Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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