ReviewDiagnostics (Basel, Switzerland)2024
Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pre-operative MRI-Based Radiomics for Predicting Telomerase Reverse Transcriptase Promoter Mutation Status in Glioma Patients: A Systematic Review and Meta-analysis.Neurosurgical review · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- CT-based Node-RADS for metastatic lymph node detection in colon cancer and influence of microsatellite instability.Abdominal radiology (New York) · 2026Article
- Artificial intelligence and radiomics in drug-induced interstitial lung disease.ERJ open research · 2026Review
- A Verifiable Framework for Brain Tumor Classification: Combining Vision Transformers, Class-Weighted Learning, and SMT-Based Formal Decision Traces.Diagnostics (Basel, Switzerland) · 2026Article
- Correction: Cè et al. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- Current applications of radiomics in neurotrauma.Neurosurgical review · 2026Review
- Machine Learning Model Based on Multiparametric MRI for Distinguishing HER2 Expression Level in Breast Cancer.Current oncology (Toronto, Ont.) · 2026Article
- AI-assisted segmentation-based fusion model integrating deep learning, radiomics, and clinical parameters for identifying coronary heart disease risk in patients with MAFLD.Frontiers in endocrinology · 2026Article
- Traditional machine learning in biomedical image analysis: before you go too deep.Frontiers in artificial intelligence · 2026Review
- Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics.Frontiers in immunology · 2026Review
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- Review
- Radiomics-based classification of pediatric dental trauma in periapical radiographs: a preliminary study.BMC medical imaging · 2025Article
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- Review
- A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.Frontiers in medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)-a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation.
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What OpenQuestion holds
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.