ArticleScientific reports2023
Comparative performances of machine learning algorithms in radiomics and impacting factors.
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 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 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
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Radiomics in ophthalmology: a systematic review.European radiology · 2025Pooled it
- Prediction of Anemia in Adenomyosis Patients Using Transvaginal Ultrasound Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Current applications of radiomics in neurotrauma.Neurosurgical review · 2026Review
- Variability Regularized Feature Selection (VaRFS) for optimal identification of robust and discriminable features from medical imaging.Npj imaging · 2026Article
- Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability.Diagnostics (Basel, Switzerland) · 2025Article
- AI-driven preclinical disease risk assessment using imaging in UK biobank.NPJ digital medicine · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%.
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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.