ArticleBMC cancer2025
Breast cancer risk assessment based on a predictive model: evaluation of risk factors among Japanese women.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEach breast cancer (BC) risk factor has different effects on different populations. However, there are no well-studied and validated BC risk prediction models for Japanese women. We developed accessible predictive models for Japanese women with optimal variables to evaluate risk factors for use by both medical institutions and women for primary BC prevention and to increase the BC screening rate. We evaluated the characteristics and distribution diversity of risk factors in this population.
methodsThis retrospective case-control study of 2,494 Japanese women included data from an original, paper-based questionnaire. The logistic regression models included 18 variables from 6 risk factors based on menopausal status (PRE, premenopausal; PERI, perimenopausal; and POST, postmenopausal). Models were evaluated based on the Akaike Information Criterion, area under the receiver operating characteristic curve (AUC), and internal validation. Bootstrap methods for bias correction in discrimination and calibration and standard deviations were calculated by the modified case-control ratio.
resultsWe created and evaluated 432 candidate models for each group. Notably, BMI, parity, FHx, and smoking history were found to increase risk in all groups. Risk-reducing factors included breastfeeding duration in the PRE and PERI models and regular alcohol consumption in the PERI and POST models. Age reduced risk in the PERI model but increased risk in the POST model. Differences were observed between PRE and PERI versus POST with respect to variable selection in parity and FHx. Our models had moderate discriminatory accuracy. AUCs (confidence intervals) of the PRE, PERI, and POST models were 0.669 (0.625-0.715), 0.669 (0.632-0.702), and 0.659 (0.627-0.693), respectively. Bias-corrected AUCs (standard deviations) were 0.697 (0.041) for PRE, 0.684 (0.033) for PERI, and 0.674 (0.031) for POST, respectively. Our models were well-calibrated after bias correction.
conclusionOur widely available, simple, and cost-effective models with optimal variables could indicate the characteristics of certain genetic and environmental risk factors for BC in Japanese women.
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