Application of Survival Quilts for prognosis prediction of gastrectomy patients based on the Surveillance,Epidemiology,and End Results database and China National Cancer Center Gastric Cancer database  

作　　者：Lulu Zhao Penghui Niu Wanqing Wang Xue Han Xiaoyi Luan Huang Huang Yawei Zhang Dongbing Zhao Jidong Gao Yingtai Chen 

机构地区：[1]Department of Pancreatic and Gastric Surgery,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China [2]Department of Cancer Prevention and Control,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China [3]Department of Breast Surgical Oncology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Shenzhen,China

出　　处：《Journal of the National Cancer Center》2024年第2期142-152,共11页癌症科学进展（英文）

基　　金：supported by grant from the National Key R&D Program of China(grant number:2017YFC0908300);the Fun-damental Research Funds for the Central Universities(grant number:3332023136).

摘　　要：Objective:Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients.We aimed to develop and test 6-month,1-,2-,3-,5-,and 10-year overall survival(OS)and cancer-specific survival(CSS)prediction models for gastric cancer patients following gastrectomy.Methods:We derived and tested Survival Quilts,a machine learning-based model,to develop 6-month,1-,2-,3-,5-,and 10-year OS and CSS prediction models.Gastrectomy patients in the development set(n=20,583)and the internal validation set(n=5,106)were recruited from the Surveillance,Epidemiology,and End Re-sults(SEER)database,while those in the external validation set(n=6,352)were recruited from the China National Cancer Center Gastric Cancer(NCCGC)database.Furthermore,we selected gastrectomy patients with-out neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis(TNM)stage.Prognostic performances of these OS and CSS models were assessed using the Concordance Index(C-index)and area under the curve(AUC)values.Results:The machine learning model had a consistently high accuracy in predicting 6-month,1-,2-,3-,5-,and 10-year OS in the SEER development set(C-index=0.861,0.832,0.789,0.766,0.740,and 0.709;AUC=0.784,0.828,0.840,0.849,0.869,and 0.902,respectively),SEER validation set(C-index=0.782,0.739,0.712,0.698,0.681,and 0.660;AUC=0.751,0.772,0.767,0.762,0.766,and 0.787,respectively),and NCCGC set(C-index=0.691,0.756,0.751,0.737,0.722,and 0.701;AUC=0.769,0.788,0.790,0.790,0.787,and 0.788,respectively).The model was able to predict 6-month,1-,2-,3-,5-,and 10-year CSS in the SEER development set(C-index=0.879,0.858,0.820,0.802,0.784,and 0.774;AUC=0.756,0.827,0.852,0.863,0.874,and 0.884,respectively)and SEER validation set(C-index=0.790,0.763,0.741,0.729,0.718,and 0.708;AUC=0.706,0.758,0.767,0.766,0.766,and 0.764,respectively).In multivariate analysis,the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in

关 键 词：Gastric cancer PROGNOSIS Survival Quilts Overall survival Cancer specific survival 

分 类 号：R73[医药卫生—肿瘤]