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

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中华医学杂志(英文版) 2024年第17期137卷 2084-2091页

作者： Liu Liyuan He Yong kao chunyu Fan Yeye Yang Fu Wang Fei Yu Lixiang Zhou Fei Xiang Yujuan Huang Shuya Zheng Chao Cai Han Bao Heling Fang Liwen Wang Linhong Chen Zengjing Yu Zhigang Department of Breast Surgery The Second Hospital Cheeloo College of Medicine Shandong University Jinan Shandong China School of Mathematics Shandong University Jinan Shandong China Zhongtai Securities Institute for Financial Studies Shandong University Jinan Shandong China Institute of Translational Medicine of Breast Disease Prevention and Treatment Shandong University Jinan Shandong China Department of Maternal and Child Health School of Public Health Peking University Haidian District Beijing China National Center for Chronic and Non-communicable Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China

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 ***: 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 ***: 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 ***: 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.

关键词： Breast cancer Cancer prevention Models Women Risk assessment

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An Improved Training Algorithm Based on Ensemble Penalized Cox Regression for Predicting Absolute Cancer Risk

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China CDC weekly 2023年第9期5卷 206-212,I0009-I0014页

作者： Liyuan Liu Fu Yang Yeye Fan chunyu kao Fei Wang Lixiang Yu Yong He Jiadong Ji Zhigang Yu Department of Breast Surgery The Second HospitalCheeloo College of MedicineShandong UniversityJinan CityShandong ProvinceChina School of Mathematics Shandong UniversityJinan CityShandong ProvinceChina Zhongtai Securities Institute for Financial Studies Shandong UniversityJinan CityShandong ProvinceChina Institute of Translational Medicine of Breast Disease Prevention and Treatment Shandong UniversityJinan CityShandong ProvinceChina.

Introduction:Biases in cancer incidence characteristics have led to significant imbalances in databases constructed by prospective cohort *** they use imbalanced databases,many traditional algorithms for training cancer risk prediction models perform ***:To improve prediction performance,we introduced a Bagging ensemble framework to an absolute risk model based on ensemble penalized Cox regression(EPCR).We then tested whether the EPCR model outperformed other traditional regression models by varying the censoring rate of the simulated ***:Six different simulation studies were performed with 100 *** assess model performance,we calculated mean false discovery rate,false omission rate,true positive rate,true negative rate,and the areas under the receiver operating characteristic curve(AUC)*** found that the EPCR procedure could reduce the false discovery rate(FDR)for important variables at the same true positive rate(TPR),thereby achieving more accurate variable *** addition,we used the EPCR procedure to build a breast cancer risk prediction model based on the Breast Cancer Cohort Study in Chinese Women *** for 3-and 5-year predictions were 0.691 and 0.642,representing improvements of 0.189 and 0.117 over the classical Gail model,***:We conclude that the EPCR procedure can overcome challenges posed by imbalanced data and improve the performance of cancer risk assessment tools.

关键词： Cancer false prediction

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