ArticleInsights into imaging2022
Evaluation of the dependence of radiomic features on the machine learning model.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers.Journal of orthopaedic surgery and research · 2023Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- B-mode ultrasound radiomics of the masseter muscle for differentiating individuals with and without clinically defined probable bruxism.Oral radiology · 2026Article
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- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- Enhancing the preoperative differentiation of ameloblastoma and odontogenic keratocyst using pathomics-guided radiomics: a pilot study.BMC oral health · 2026Article
- Rethinking feature reproducibility in radiomics: the elephant in the dark.European radiology experimental · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study.Cancers · 2025Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?Computational and structural biotechnology journal · 2024Article
- Image biomarkers and explainable AI: handcrafted features versus deep learned features.European radiology experimental · 2024Review
- Computational analysis of variability and uncertainty in the clinical reference on magnetic resonance imaging radiomics: modelling and performance.Visual computing for industry, biomedicine, and art · 2024Article
- Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences.Insights into imaging · 2024Article
- Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake.Bioengineering (Basel, Switzerland) · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- The effect of feature normalization methods in radiomics.Insights into imaging · 2024Article
- Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.BMC cancer · 2023Article
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1 author.
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Abstract
backgroundIn radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features.
resultsApproximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35-0.84). The features selected by different models varied largely (0.19; range 0.02-0.42), although the selected features themselves were highly correlated (0.71; range 0.4-0.92).
conclusionsFeature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.
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