ArticleScientific reports2023
Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Hunting for signal in the noise: identifying robust CT radiomic features across variable scanning settings.European radiology · 2026Article
- Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics.Frontiers in immunology · 2026Review
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Repeatability and prognostic value of radiomic features: a study in esophageal cancer and nasopharyngeal carcinoma.Insights into imaging · 2025Article
- A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma.La Radiologia medica · 2025Article
- Establishment of a CT-based radiomic feature robustness databank for OPC patients via image perturbation in a multi-institutional study: a practical method to safeguard model generalizability.Frontiers in oncology · 2025Article
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.Diagnostics (Basel, Switzerland) · 2024Review
- Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.Breast cancer research : BCR · 2024Article
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13 authors.
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Abstract
Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.7-0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.
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