Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

作者：Xiaorong Zhong, Ting Luo, Ling Deng, Pei Liu, Kejia Hu, Donghao Lu, Dan Zheng, Chuanxu Luo, Yuxin Xie, Jiayuan Li, Ping He, Tianjie Pu, Feng Ye, Hong Bu, Bo Fu, Hong Zheng · 发表于：JMIR Medical Informatics · 年份：2020 · DOI：10.2196/19069 · 被引用次数：19 · 研究领域：AI in cancer detection、Breast Cancer Treatment Studies、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. OBJECTIVE: We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. METHODS: This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. RESULTS: The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 14...