252Development and validation of a breast cancer absolute risk prediction model in Chinese population
Author(s) -
Yuting Han,
Jun Lv,
Canqing Yu,
Yu Guo,
Zheng Bian,
Yizhen Hu,
Ling Yang,
Yiping Chen,
Huaidong Du,
Fangyuan Zhao,
Wanqing Wen,
XiaoOu Shu,
YongBing Xiang,
YuTang Gao,
Wei Zheng,
Junshi Chen,
Zhengming Chen,
Dezheng Huo,
Liming Li
Publication year - 2021
Publication title -
international journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.406
H-Index - 208
eISSN - 1464-3685
pISSN - 0300-5771
DOI - 10.1093/ije/dyab168.259
Subject(s) - medicine , breast cancer , demography , confidence interval , prospective cohort study , population , cohort , incidence (geometry) , residence , cohort study , cancer , gynecology , environmental health , sociology , optics , physics
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.
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