252Development and validation of a breast cancer absolute risk prediction model in Chinese population. (2nd September 2021)

• Record Type: Journal Article
• Authors: Han, Yuting; Lv, Jun; Yu, Canqing; Guo, Yu; Bian, Zheng; Hu, Yizhen; Yang, Ling; Chen, Yiping; Du, Huaidong; Zhao, Fangyuan; Wen, Wanqing; Shu, Xiao-Ou; Xiang, Yongbing; Gao, Yu-Tang; Zheng, Wei; Chen, Junshi; Chen, Zhengming; Huo, Dezheng; Li, Liming
• Is Part Of: International journal of epidemiology. Volume 50(2021)Supplement 1
• Publication Date: 2021-09-02
• Subjects: Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab168.259
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Background: Compared with developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate, lower survival rate, and vast geographic variation. However, there is no national validated model in China to aid early detection yet. Methods: A large nation-wide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks. A total of 300, 824 women free of prior cancer were recruited during 2004-2008 and followed up to 31 December 2016. Absolute risks were calculated by incorporating national age- and residence-specific incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), to externally validate the calibration and discriminating accuracy. Results: During a median of 10.2 years of follow-up in the CKB, 2, 287 cases were observed. The final model included education, BMI, height, family history of cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed ratios of 1.00 (95% confidence interval (CI), 0.96-1.04) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the adjusted areas under the curve were 0.615 (95% CI, 0.600-0.630) and 0.585 (95% CI, 0.564-0.605), respectively. Conclusions: Based only on non-laboratory predictors, our model has an excellent calibration and moderate discriminating capacity. The model may be a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies. Key messages: We developed a breast cancer prediction model for Chinese women, which performed well in internal and external validation.