基于肿瘤负荷评分、血清学指标对肝癌微血管侵犯术前预测模型的建立  

Establishment of preoperative prediction model for microvascular invasion of liver cancer based on tumor burden score and serological indicators

作　　者：朱明强 杨大帅 裴俊鹏 熊祥云 苏洋 丁佑铭[1] ZHU Mingqiang;YANG Dashuai;PEI Junpeng;XIONG Xiangyun;SU Yang;DING Youming(Department of Hepatobiliary Surgery,Renmin Hospital of Wuhan University,Wuhan 430060,China)

机构地区：[1]武汉大学人民医院肝胆外科,湖北武汉430060

出　　处：《胃肠病学和肝病学杂志》2023年第11期1249-1253,共5页Chinese Journal of Gastroenterology and Hepatology

基　　金：国家重点研发计划(2022YFC2407304)。

摘　　要：目的探讨肿瘤负荷评分(tumor burden score,TBS)联合中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)、白蛋白与碱性磷酸酶比值(albumin-to-alkaline phosphatase ratio,AAPR)等血清学指标对肝癌微血管侵犯(microvascular invasion,MVI)的术前预测模型的建立。方法回顾性收集2020年1月至2022年8月于武汉大学人民医院行肝切除术的200例原发性肝癌患者的临床资料,根据术后病理检测是否存在MVI,将患者分为MVI组(n=74)和非MVI组(n=126)。采用Logistic回归分析筛选肝癌患者发生MVI的影响因素,并建立肝癌患者术前是否发生MVI的列线图风险预测模型。将所有患者按7∶3随机分为训练集(n=140)和验证集(n=60),采用Bootstrap法对模型进行内部验证,应用模型校准曲线和ROC曲线来评价列线图模型的校准度和区分度。结果200例患者中74例(37.0%)发生MVI。Logistic多因素分析显示,TBS、AAPR、术前NLR、AFP、CONUT评分是肝癌患者发生MVI的独立危险因素(P均<0.05)。基于上述5个独立危险因素构建的列线图,训练集的C指数为0.794,ROC曲线下面积为0.791;验证集的C指数为0.756,ROC曲线下面积为0.762(P均<0.05)。列线图模型校准曲线显示预测值与实际观测值基本一致,表明列线图模型预测的准确度较好。ROC曲线显示此列线图模型用于预测术前MVI有较好的区分度。结论基于上述5个独立危险因素建立的个体化列线图风险预测模型对肝癌MVI的术前预测效能良好。Objective To investigate the establishment of a preoperative prediction model for microvascular invasion(MVI)of liver cancer by tumor burden score(TBS)combined with serological indicators such as neutrophil-to-lymphocyte ratio(NLR)and albumin-to-alkaline phosphatase ratio(AAPR).Methods The clinical data of 200 patients with primary liver cancer who underwent hepatectomy in Renmin Hospital of Wuhan University,from Jan.2020 to Aug.2022 were retrospectively collected,and all patients were divided into MVI group(n=74)and non-MVI group(n=126)according to the postoperative pathological detection of MVI.Logistic regression analysis were used to screen the risk factors of MVI in liver cancer patients,and a nomogram risk prediction model for whether MVI occurred before surgery in liver cancer patients was established.All patients were randomly divided into training cohort(n=140)and validation cohort(n=60)according to 7∶3,the model was internally verified by Bootstrap method,and the calibration curve of the model and the ROC curve were used to evaluate the calibration and discrimination of the nomogram model.Results MVI occurred in 74 cases(37.0%)of the 200 patients.The results of Logistic multivariate analysis showed that TBS,AAPR,preoperative NLR,AFP and CONUT scores were independent risk factors for MVI in patients with liver cancer(P<0.05).Based on the above five independent risk factors,the C index of the training cohort was 0.794,the area under the ROC curve was 0.791,the C index of the verification cohort was 0.756,the area under the ROC curve was 0.762(P<0.05).The calibration curve of the nomogram model showed that the predicted value was basically consistent with the actual observed value,indicating that the prediction accuracy of the nomogram model was good.The ROC curve showed that this nomogram model was well differentiated for predicting preoperative MVI.Conclusion The individualized nomogram risk prediction model based on the above five independent risk factors has good efficacy in predicting MVI of liver c

关 键 词：肝癌 微血管侵犯 肿瘤负荷评分 血清学指标 危险因素 列线图 

分 类 号：R735.5[医药卫生—肿瘤]