Risk prediction models for breast cancer: a systematic review - Jiang Li; He Li; Jie He; Ni Li; Yadi Zheng; Zheng Wu; Maomao Cao (BMJ Open)
Objectives To systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.Design Systematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sources PubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteria We included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. Search was limited to English language only.Data extraction and synthesis Two reviewers independently screened, reviewed, extracted and assessed studies with discrepancies resolved through discussion or a third reviewer. Risk of bias was assessed according to the PROBAST framework.Results 63 894 studies were screened and 40 studies with 47 risk prediction models were included in the review. Most of the studies used logistic regression to develop breast cancer risk prediction models for Caucasian women by case–control data. The most widely used risk factor was reproductive factors and the highest area under the curve was 0.943 (95% CI 0.919 to 0.967). All the models included in the review had high risk of bias.Conclusions No risk prediction models for breast cancer were recommended for different ethnic groups and models incorporating mammographic density or single-nucleotide polymorphisms among Asian women are few and poorly needed. High-quality breast cancer risk prediction models assessed by PROBAST should be developed and validated, especially among Asian women.PROSPERO registration number CRD42020202570.