Ⅱ期结肠癌切除术后辅助化疗的预后研究

Predicting prognosis of resected stageⅡcolon cancer patients with adjuvant chemotherapy

机器学习可以提高精准医疗的深度和广度。本文拟用机器学习预测Ⅱ期结肠癌患者行切除术后接受辅助化疗的预后情况。从美国监测、流行病学和结局数据库(SEER数据库),回顾性收集1166例Ⅱ期结肠癌患者行切除术后接受辅助化疗的病例,并根据随访结果将病人分为死亡组和非死亡组。通过单因素和多因素Cox回归分析影响患者总生存期的独立影响因子,并根据Cox回归系数建立诺谟图,计算每个患者的总得分,辅助临床应用。Cox比例风险模型进行单因素分析发现淋巴结数量、存在大于4个阳性淋巴结、浸润深度、肠粘连、手术时间、肿瘤分化程度和辅助化疗是影响患者生存的单因素。利用Cox比例风险模型对纳入的单因素进行多因素分析显示淋巴结数量(P=0.028,OR=1.365[0.999~1.142])、手术时间(P=0.01,OR=1.452[1.094~1.93])、存在>4个阳性淋巴结(P<0.001,OR=2.54[1.574~4.08])、肿瘤浸润至浆膜层(P=0.018,OR=1.411[1.1~2.702])、肿瘤浸润至基层(P=0.006,OR=2.88[1.312~3.877])、LV+5-FU辅助化疗(P=0.003,OR=0.595[0.464~0.762])是影响患者生存的独立影响因素。基于Cox回归系数建立的诺谟图C指数为0.76。用筛选的独立影响因子建立5折XGBoost预后预测模型,平均AUC为0.817±0.012。利用机器学习可以精准地预测Ⅱ期结肠癌切除术后辅助化疗的预后,从而为患者提供精准治疗。

Machine learning could improve the depth and breadth of precision medicine.This article intends to use machine learning to predict the prognosis of stageⅡcolon cancer patients receiving adjuvant chemotherapy after resection.We retrospectively collected 1166 cases from Surveillance,Epidemiology,and End Results(SEER)and the patients were divided into death or non-death groups according to follow-up results.Univariate and multivariate Cox regression analysis was used to find out the independent prognosis factors.Independent factors affecting the overall survival of patients were analyzed by univariate and multivariate Cox regression analysis,and a nomogram was developed based on Cox regression coefficients to calculate the total score of each patient for clinical application.Univariate Cox proportional hazards models revealed that lymph node number,>4 positive lymph nodes,extent of local spread,operation time,intestinal tumor differentiation,and adjuvant chemotherapy regimen were single factors that affected patient survival.Multivariate analysis of the single factors included in the Cox proportional hazards model showed that lymph node number(P=0.028,OR=1.365[0.999-1.142]),operation time(P=0.01,OR=1.452[1.094-1.93]),>4 positive lymph nodes(P<0.001,OR=2.54[1.574-4.08]),tumor infiltration of the serosal layer(P=0.018,OR=1.411[1.1-2.702]),tumor infiltration of the submucosal layer(P=0.006,OR=2.88[1.312-3.877]),and LV+5-FU adjuvant(P=0.003,OR=0.595[0.464-0.762])were independent factors affecting patient survival.The C index of the nomogram based on the Cox regression coefficients was 0.73.A 5-fold XGBoost prognostic prediction model was established based on selected independent factors,with an average AUC of 0.817±0.012.Machine learning can accurately predict the prognosis of patients with stageⅡcolon cancer after surgery and adjuvant chemotherapy,thereby providing accurate treatment for patients.