ArticleBritish journal of cancer2020
Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations.
Article in British journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 6 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Global guidelines for breast cancer screening: A systematic review.Breast (Edinburgh, Scotland) · 2022Pooled it
- Performance of Statistical and Machine Learning Risk Prediction Models for Advanced Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Machine learning-based single-sample molecular classifier for cancer grading.Frontiers in oncology · 2025Article
- Pregnancy-Related Factors and Breast Cancer Risk for Women Across a Range of Familial Risk.JAMA network open · 2024Article
- Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Article
- An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline.Archives of toxicology · 2022Article
- The current status of risk-stratified breast screening.British journal of cancer · 2022Review
- Review
- Study the Effect of the Risk Factors in the Estimation of the Breast Cancer Risk Score Using Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2021Article
- An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?Journal of geriatric oncology · 2021Article
- Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.BMC medical informatics and decision making · 2021Article
- Artificial intelligence for clinical oncology.Cancer cell · 2021Review
- Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations.British journal of cancer · 2021Review
- Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer.Cancer control : journal of the Moffitt Cancer CenterArticle
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6 authors.
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Abstract
backgroundThe clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices.
methodsWe used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20-80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol.
resultsThe predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as 'near population' risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50.
conclusionML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening.
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