肿瘤综合治疗电子杂志2024，Vol.10Issue(2)：117-125,9.DOI:10.12151/JMCM.2024.02-12
预测乳腺癌术后死亡的两种模型建立及其性能和临床应用价值对比
Establishment of two models for predicting postoperative death of breast cancer and comparison of their performance and clinical application value
摘要
Abstract
Objective To compare the predictive performance of random survival forest(RSF)and Cox regression on postoperative mortality risk of breast cancer patients.Method The clinical data of 482 patients with primary breast cancer treated by surgery in the Affiliated Hospital of Xuzhou Medical University from January 2014 to December 2016 were retrospectively analyzed.According to their survival during the follow-up period,they were divided into survival group(n= 446)and death group(n= 36)respectively.The data such as age,body mass index(BMI),clinical stage and estrogen receptor(ER)status were compared between the two groups.The factors with statistical differences between the two groups were established by RSF and Cox regression.Receiver operating characteristic curve(ROC curve)was used to evaluate the area under the curve(AUC)and efficacy of the two models in predicting the postoperative mortality risk of breast cancer patients,and Kaplan-Meier curve was used to evaluate the value of the two models in guiding risk stratification.Finally,an online dynamic prognostic prediction analysis website was developed based on the RSF model.Result All patients were followed up effectively until November,2023.The median follow-up time was 93.30 months,and 36 patients died during the follow-up.Multivariate Cox regression showed that preoperative fasting blood glucose(FBG),preoperative uric acid(UA),axillary lymph node metastasis and ER expression were related to postoperative death risk of breast cancer patients(all P＜0.05).The optimal model of RSF included ten variables:namely,age,N stage,pathological grade,preoperative UA,maximum tumor diameter,preoperative FBG,preoperative glycoprotein 153,preoperative carcinoembryonic antigen(CEA),radiation therapy,and chemotherapy regimen.In the dataset,the AUC,sensitivity,specificity and of RSF model at 1 year,3 years and 5 years were significant higher than that of Cox regression model(all P＜0.05),while Brier scores were significant lower than that of Cox regression model,so the overall prediction performance was significant better than that of Cox regression model(P＜0.05).After stratification of patients'risk,the survival difference between the low-risk group and the high-risk group of the two models were statistically significant(all P＜0.05).The online dynamic prognostic prediction analysis website was easy to operate.Clinicians could directly obtain the prognosis and survival of patients by filling in relevant information.Conclusion The models based on RSF and Cox regression can provide reliable reference for predicting the risk of postoperative death of breast cancer patients,but the predictive performance and stability of RSF model are slightly better than Cox model,which has higher clinical significance and value combined with real-time online dynamic prediction of overall patient survival and the risk of death at different time points.关键词
乳腺癌/随机生存森林/Cox回归/死亡风险/预测模型Key words
Breast cancer/Random survival forest/Cox regression/Risk of death/Prediction model引用本文复制引用
丁伟,刘钊,刘小华,许雪宁,吕帝,陈佳宇,侯先存..预测乳腺癌术后死亡的两种模型建立及其性能和临床应用价值对比[J].肿瘤综合治疗电子杂志,2024,10(2):117-125,9.基金项目
吴阶平医学基金会临床科研专项资助基金(320.6750.2022-19-6) （320.6750.2022-19-6）
