An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study

被引：0

作者：

Liu Liyuan ^{[1
,2
]}

He Yong ^{[2
,3
]}

Kao Chunyu ^{[3
]}

Fan Yeye ^{[2
]}

Yang Fu ^{[3
]}

Wang Fei ^{[1
,4
]}

Yu Lixiang ^{[1
,4
]}

Zhou Fei ^{[1
,4
]}

Xiang Yujuan ^{[1
,4
]}

Huang Shuya ^{[1
,4
]}

Zheng Chao ^{[1
,4
]}

Cai Han ^{[1
,4
]}

Bao Heling ^{[5
]}

Fang Liwen ^{[6
]}

Wang Linhong ^{[6
]}

Chen Zengjing ^{[2
]}

Yu Zhigang ^{[1
,4
]}

机构：

[1] Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China

[2] School of Mathematics, Shandong University, Jinan, Shandong, China

[3] Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong, China

[4] Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong, China

[5] Department of Maternal and Child Health, School of Public Health, Peking University, Haidian District, Beijing, China

[6] National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China

来源：

中华医学杂志英文版 | 2024年 / 137卷 / 17期

关键词：

Breast cancer; Cancer prevention; Models; Women; Risk assessment;

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习]; R737.9 [乳腺肿瘤];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ; 100214 ;

摘要：

Background: Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods: The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results: The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions: We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

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