Development and external validation of a breast cancer absolute risk prediction model in Chinese population
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 , Guo Hong , Liang Peng , Chen Junshi , Chen Zhengming , Huo Dezheng , Li Liming ,   on behalf of the China Kadoorie Biobank Collaborative Group, Chen Junshi , Chen Zhengming , Clarke Robert , Collins Rory , Guo Yu , Li Liming , Lv Jun , Peto Richard , Walters Robin , Avery Daniel , Boxall Ruth , Bennett Derrick , Chang Yumei , Chen Yiping , Chen Zhengming , Clarke Robert , Du Huaidong , Gilbert Simon , Hacker Alex , Hill Mike , Holmes Michael , Iona Andri , Kartsonaki Christiana , Kerosi Rene , Kong Ling , Kurmi Om , Lancaster Garry , Lewington Sarah , Lin Kuang , McDonnell John , Millwood Iona , Nie Qunhua , Radhakrishnan Jayakrishnan , Ryder Paul , Sansome Sam , Schmidt Dan , Sherliker Paul , Sohoni Rajani , Stevens Becky , Turnbull Iain , Walters Robin , Wang Jenny , Wang Lin , Wright Neil , Yang Ling , Yang Xiaoming , Bian Zheng , Guo Yu , Han Xiao , Hou Can , Lv Jun , Pei Pei , Liu Chao , Yu Canqing , Pang Zengchang , Gao Ruqin , Li Shanpeng , Wang Shaojie , Liu Yongmei , Du Ranran , Zang Yajing , Cheng Liang , Tian Xiaocao , Zhang Hua , Zhai Yaoming , Ning Feng , Sun Xiaohui , Li Feifei , Lv Silu , Wang Junzheng , Hou Wei , Zeng Mingyuan , Jiang Ge , Zhou Xue , Yang Liqiu , He Hui , Yu Bo , Li Yanjie , Xu Qinai , Kang Quan , Guo Ziyan , Wang Dan , Hu Ximin , Chen Jinyan , Fu Yan , Fu Zhenwang , Wang Xiaohuan , Weng Min , Guo Zhendong , Wu Shukuan , Li Yilei , Li Huimei , Fu Zhifang , Wu Ming , Zhou Yonglin , Zhou Jinyi , Tao Ran , Yang Jie , Su Jian , Liu Fang , Zhang Jun , Hu Yihe , Lu Yan , Ma Liangcai , Tang Aiyu , Zhang Shuo , Jin Jianrong , Liu Jingchao , Tang Zhenzhu , Chen Naying , Huang Ying , Li Mingqiang , Meng Jinhuai , Pan Rong , Jiang Qilian , Lan Jian , Liu Yun , Wei Liuping , Zhou Liyuan , Chen Ningyu , Wang Ping , Meng Fanwen , Qin Yulu , Wang Sisi , Wu Xianping , Zhang Ningmei , Chen Xiaofang , Zhou Weiwei , Luo Guojin , Li Jianguo , Chen Xiaofang , Zhong Xunfu , Liu Jiaqiu , Sun Qiang , Ge Pengfei , Ren Xiaolan , Dong Caixia , Zhang Hui , Mao Enke , Wang Xiaoping , Wang Tao , Zhang Xi , Zhang Ding , Zhou Gang , Feng Shixian , Chang Liang , Fan Lei , Gao Yulian , He Tianyou , Sun Huarong , He Pan , Hu Chen , Zhang Xukui , Wu Huifang , He Pan , Yu Min , Hu Ruying , Wang Hao , Qian Yijian , Wang Chunmei , Xie Kaixu , Chen Lingli , Zhang Yidan , Pan Dongxia , Gu Qijun , Huang Yuelong , Chen Biyun , Yin Li , Liu Huilin , Fu Zhongxi , Xu Qiaohua , Xu Xin , Zhang Hao , Long Huajun , Li Xianzhi , Zhang Libo , Qiu Zhe 
Published: July 2021

AbstractBackgroundsIn contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet.MethodsA large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy.ResultsDuring a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively.ConclusionsBased only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.