SynthesisJMIR public health and surveillance2022
An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.
Synthesis in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe.BMC cancer · 2023Pooled it
- Interpretable depressive symptoms screening via statistical reasoning-augmented large language models using wearable and environmental data.Scientific reports · 2026Article
- An interpretable machine learning model for preoperative prediction of renal mass malignancy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Article
- Optimization of ultrasonic extraction and kinetic modeling of jieduquyuziyin prescription polysaccharides via explainable machine learning.Ultrasonics sonochemistry · 2025Article
- Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach.JMIR cancer · 2025Article
- Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data.Cancer reports (Hoboken, N.J.) · 2025Article
- Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study.JMIR cancer · 2025Article
- The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis.Frontiers in oncology · 2025Article
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Accelerated hazard prediction based on age time-scale for women diagnosed with breast cancer using a deep learning method.BMC medical informatics and decision making · 2024Article
- Article
- Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.Cancers · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies have explored the predictive performance of machine learning-based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning-based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models.
objectiveThe aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning-based breast cancer risk prediction models.
methodsWe searched for papers published until June 9, 2021, on machine learning-based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model.
resultsA total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning-based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I
conclusionsOur review shows that the current machine learning-based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.
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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.