中青年肝癌患者根治术后不同复发时期的风险模型构建及验证  

Construction and validation of risk model for young and middle-aged patients with liver cancer at different recurrence stages after radical operation

作　　者：李娅辉 栾琳 黄辉云 Li Yahui;Luan Lin;Huang Huiyun(Department of Infectious Diseases,Qingdao Eighth People's Hospital,Qingdao 266000,China)

机构地区：[1]青岛市第八人民医院感染性疾病科,山东省266000

出　　处：《中华消化病与影像杂志(电子版)》2025年第2期112-119,共8页Chinese Journal of Digestion and Medical Imageology(Electronic Edition)

摘　　要：目的构建和验证针对中青年肝癌患者根治术后不同时期复发的预测模型,为临床提供更有效的术后监测和干预策略。方法选取2017年1月至2021年12月青岛市第八人民医院接受根治术治疗的341例原发性肝细胞癌(PHC)作为研究对象,按照一定的纳入和排除标准筛选病例,并收集详细的临床资料。采用逻辑回归(LR)、决策树(DT)、支持向量机(SVM)、随机森林(RF)和极端梯度提升算法(XGBoost)五种机器学习算法构建预测模型,将所有患者分为训练集和验证集,训练集数据通过五折交叉验证的方式训练、优化各模型。采用测试集数据对各模型的预测性能进行评估,评价方法采用受试者工作特征曲线(ROC)进行,统计各模型预测近期复发、远期复发的曲线下面积(AUC)、敏感度、特异度及约登指数。结果341例PHC患者中,共有173例(50.73%)出现了术后复发,其中78例(22.87%)为近期复发,95例(27.86%)为远期复发。近期复发者、远期复发者的中性粒细胞与淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)、丙氨酸转氨酶、天冬氨酸转氨酶、总胆红素、甲胎蛋白水平分别明显高于近期未复发者、远期未复发者,同时近期复发者与近期未复发者、远期复发者与远期未复发者在合并门静脉癌栓、肿瘤包膜完整性、BCLC分期和肿瘤分化程度等方面均存在显著差异(P<0.05)。模型预测结果显示,XGBoost算法在预测近期复发(AUC=0.989)和远期复发(AUC=0.983)方面表现出最佳性能,其次是RF(AUC分别为0.926、0.939)和SVM算法(AUC分别为0.914、0.904)。结论LR、DT、SVM、RF和XGBoost均能够较好预测中青年肝癌患者根治术后不同时期复发的可能性。其中RF、SVM和XGBoost模型的预测性能相对较好,尤其是XGBoost模型展现出较高的预测准确性。另外,NLR、PLR、相关肝功能指标以及门静脉癌栓、肿瘤包膜完整性、BCLC分期以及肿瘤分化程度可能是影�Objective To construct and verify the prediction model for recurrence of young and middle-aged patients with liver cancer in different periods after radical surgery,and then provide more effective postoperative monitoring and intervention strategies for clinic.Methods From January 2017 to December 2021,341 cases of primary hepatocellular carcinoma(PHC)were selected as the research object,and the cases were screened according to certain inclusion and exclusion criteria,and detailed clinical data were collected.Five machine learning algorithms,including logistic regression(LR),decision tree(DT),support vector machine(SVM),random forest(RF)and extreme gradient lifting algorithm(XGBoost),were used to construct the prediction model.All patients were divided into training set and validation set,and the data of training set were trained and optimized by 50%cross validation.Receiver operating characteristic curve(ROC)was used to evaluate the prediction performance of each model,and the area under the curve(AUC),sensitivity,specificity and Jordan index of each model for predicting recent recurrence and long-term recurrence were counted.Results Of the 341 PHC patients,173(50.73%)had postoperative recurrence,of which 78(22.87%)had recent recurrence and 95(27.86%)had long-term recurrence.The levels of neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),alanine aminotransferase,aspartate transaminase,total bilirubin and alpha fetoprotein in recent and long-term relapses were significantly higher than those in patients without recent and long-term relapses.At the same time,there were significant differences between those with recent recurrence and those without recent recurrence,and between those with long-term recurrence and those without long-term recurrence in portal vein tumor thrombus,tumor envelope integrity,BCLC staging and tumor differentiation(P<0.05).The model prediction results showed that XGBoost algorithm had the best performance in predicting recent recurrence(AUC=0.989)and long-term recurrence(

关 键 词：肝细胞癌 肿瘤复发 风险模型 中青年人 机器学习算法 

分 类 号：R735.7[医药卫生—肿瘤]