ReviewDiagnostic and interventional radiology (Ankara, Turkey)2025
Reproducibility and interpretability in radiomics: a critical assessment
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Artificial intelligence for the prediction of synchronous and metachronous liver metastasis in colorectal cancer patients: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.Molecular imagingPooled it
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- Radiomics in musculoskeletal imaging: what is its role in the era of deep learning?Skeletal radiology · 2026Article
- Externally validated yet undertrained: sample size deficits in machine learning-based radiomics.European radiology · 2026Article
- Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.Journal of thoracic disease · 2026Article
- Pericoronary fat radiomics on coronary CT angiography for predicting major adverse cardiac events: a systematic review and meta-analysis.European radiology · 2026Article
- Longitudinal Evolution of Radiomic Features in Radiation-Induced Necrosis During Follow-Up of Brain Metastases: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study.JMIR formative research · 2026Article
- Magnetic Resonance Imaging-Based Radiomic Signatures for the Diagnosis of Zoster-Associated Pain: A Quantitative Imaging Approach.Pain and therapy · 2026Article
- Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- Retractions of publications in radiomics: An underestimated problem?European radiology · 2026Review
- Epicardial Adipose Tissue CT Radiomics Improves Acute Coronary Syndrome Prediction Beyond Coronary Artery Calcium Score.Diagnostics (Basel, Switzerland) · 2026Article
- Impact of partial volume correction on radiomics reproducibility in theranostic SPECT/CT imaging.Medical physics · 2026Article
- Habitat-based CT radiomics profiling spatial-temporal heterogeneity in resectable NSCLC predict pathological response to neoadjuvant chemoimmunotherapy: a multi-center study.NPJ precision oncology · 2026Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics aims to improve clinical decision making through the use of radiological imaging. However, the field is challenged by reproducibility issues due to variability in imaging and subsequent statistical analysis, which particularly affects the interpretability of the model. In fact, radiomics extracts many highly correlated features that, combined with the small sample sizes often found in radiomics studies, result in high-dimensional datasets. These datasets, which are characterized by containing more features than samples, have different statistical properties than other datasets, thereby complicating their training by machine learning and deep learning methods. This review critically examines the challenges of both reproducibility issues and interpretability, beginning with an overview of the radiomics pipeline, followed by a discussion of the imaging and statistical reproducibility issues. It further highlights how limited model interpretability hinders clinical translation. The discussion concludes that these challenges could be mitigated by following best practices and by creating large, representative, and publicly available datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.