ReviewJournal of magnetic resonance imaging : JMRI2020
Machine learning in breast MRI.
Review in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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
69 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts.European radiology · 2024Trial
- Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
- Multiparametric MRI deep learning model based on dynamic Contrast-enhanced and apparent diffusion coefficient map enables accurate prediction of benign and malignant breast lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Enhancing breast magnetic resonance imaging segmentation with a federated semi-supervised approach.Scientific reports · 2025Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- MRI Radiomics for Predicting the Diffuse Type of Tenosynovial Giant Cell Tumor: An Exploratory Study.Diagnostics (Basel, Switzerland) · 2025Article
- Prediction of Germline BRCA Mutations in High-Risk Breast Cancer Patients Using Machine Learning with Multiparametric Breast MRI Features.Sensors (Basel, Switzerland) · 2025Article
- A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics.Cancers · 2025Article
- Article
- 3.0 T multi-parametric MRI combined with clinical features improve malignancy prediction of BI-RADS 4 lesions and preoperative prediction of Nottingham Prognostic Index.European journal of radiology open · 2025Article
- Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation.BMC medical imaging · 2025Article
- Article
- Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?Diagnostics (Basel, Switzerland) · 2025Review
- Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review.Brain & spine · 2025Review
- Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: a retrospective study.Radiologie (Heidelberg, Germany) · 2024Article
- Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer.Heliyon · 2024Article
- A Review of Machine Learning Algorithms for Biomedical Applications.Annals of biomedical engineering · 2024Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
Indexed as
Identifiers
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.