ArticleJournal of the National Cancer Institute2018
Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening.
Article in Journal of the National Cancer Institute, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for malignancy upgrade in high-risk breast lesions: a qualitative systematic review.Breast cancer research and treatment · 2025Pooled it
- Development of a Screening Model for Exercise-Induced Desaturation by Machine Learning Method.Tuberculosis and respiratory diseases · 2026Article
- Validation of a Colorectal Cancer Risk Prediction Model in US Black Women.Cancer prevention research (Philadelphia, Pa.) · 2026Article
- Performance of clinical breast cancer risk prediction models vs a mammography-based artificial intelligence risk model.Journal of the National Cancer Institute · 2026Article
- Contextualizing the Utility of Polygenic Risk Scores using Absolute Risk Models in Diverse Ancestry Populations.medRxiv : the preprint server for health sciences · 2026Article
- Effect of race and ethnicity on advanced breast cancer risk prediction model performance.NPJ digital medicine · 2025Article
- Under-recognized risk factors and low perceived breast cancer risk among rural women in South-central China: A multisite cross-sectional study.Asia-Pacific journal of oncology nursing · 2025Article
- Association between prediagnostic serum metabolites and pancreatic ductal adenocarcinoma risk in two prospective cohorts.International journal of cancer · 2025Article
- Personalized screening based on risk and density: prevalence data from the RIBBS study.La Radiologia medica · 2025Observational
- Prediction of breast cancer risk for adolescents and young adults with Hodgkin lymphoma.Journal of the National Cancer Institute · 2025Article
- Building absolute breast cancer risk prediction models for women treated with chest radiation for Hodgkin lymphoma.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Software Application Profile: CaseCohortCoxSurvival-an R package for case-cohort inference for relative hazard and pure risk under the Cox model.International journal of epidemiology · 2025Article
- Nationwide study on development and validation of a risk prediction model for CIN3+ and cervical cancer in Estonia.Scientific reports · 2024Article
- The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women.La Radiologia medica · 2024Article
- Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China.BMC cancer · 2023Article
- Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway.Cancers · 2023Article
- Techniques to produce and evaluate realistic multivariate synthetic data.Scientific reports · 2023Article
- Article
- Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study.Radiology · 2023Observational
Corrections and comments
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2 authors.
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Abstract
Background: Incorporation of polygenic risk scores and mammographic density into models to predict breast cancer incidence can increase discriminatory accuracy (area under the receiver operating characteristic curve [AUC]) from 0.6 for models based only on epidemiologic factors to 0.7. It is timely to assess what impact these improvements will have on individual counseling and on public health prevention and screening strategies, and to determine what further improvements are needed. Methods: We studied various clinical and public health applications using a log-normal distribution of risk. Results: Provided they are well calibrated, even risk models with AUCs of 0.6 to 0.7 provide useful perspective for individual counseling and for weighing the harms and benefits of preventive interventions in the clinic. At the population level, they are helpful for designing preventive intervention trials, for assessing reductions in absolute risk from reducing exposure to modifiable risk factors, and for resource allocation (although a higher AUC would be desirable for risk-based allocation). Other public health applications require higher AUCs that can only be achieved with risk predictors 1.6 to 8.8 times as strong as all those yet discovered combined. Such applications are preventing an appreciable proportion of population disease when employing a high-risk prevention strategy and deciding who should be screened for subclinical disease. Conclusions: Current and foreseeable risk models are useful for counseling and some prevention activities, but given the daunting challenge of achieving, for example, an AUC of 0.8, considerable effort should be put into finding effective preventive interventions and screening strategies with fewer adverse effects.
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