基于检验数据和机器学习构建并验证术前肝细胞癌微血管浸润的预测模型  

Development and validation of a machine-learning model based on routine laboratory parameters for preoperative prediction of microvascular invasion in patients with hepatocellular carcinoma

作　　者：于洲 林丽金 陈雅芝 林天生[4] 欧启水[1] 黄金兰 Yu Zhou;Lin Lijin;Chen Yazhi;Lin Tiansheng;Ou Qishui;Huang Jinlan(Department of Laboratory Medicine,Fujian Key Laboratory of Laboratory Medicine,Gene Diagnosis Research Center,the First Affiliated Hospital,Fujian Medical University,Fuzhou350005,China;The First School of Clinical Medicine,Fujian Medical University,Fuzhou350005,China;Department of Laboratory Medicine,National Regional Medical Center,Binhai Campus of the First Affiliated Hospital,Fujian Medical University,Fuzhou,Fujian350212,China;Department of Hepatobiliary Surgery,Fujian Provincial Hospital,Fuzhou University Affiliated Provincial Hospital,Shengli Clinical Medical College of Fujian Medical University,Fuzhou350001,China)

机构地区：[1]福建医科大学附属第一医院检验科、福建省检验医学重点实验室、福建医科大学基因诊断研究中心、福建省临床免疫学检验临床医学研究中心,福州350005 [2]福建医科大学第一临床医学院,福州350005 [3]福建医科大学附属第一医院滨海院区国家区域医疗中心检验科,福州350212 [4]福建医科大学省立临床医学院、福建省立医院、福州大学附属省立医院肝胆外科,福州350001

出　　处：《中华检验医学杂志》2025年第1期65-75,共11页Chinese Journal of Laboratory Medicine

基　　金：国家自然科学基金(82372325);福建省卫健委中青年骨干人才培养项目(2021GGA023);福建省自然科学基金(2022J01680)。

摘　　要：目的利用机器学习方法,基于检验指标建立并验证术前肝细胞癌(HCC)微血管浸润(MVI)的预测模型。方法纳入2019年1月至2023年12月在福建医科大学附属第一医院经术后病理确诊为HCC的患者629例,将2019年1月至2022年12月患者纳入至训练集(n=464),将2023年1—12月患者纳入内部验证集(n=165)。2023年1—12月在福建省立医院经术后病理确诊为HCC的患者190例作为外部验证集。收集所有患者年龄、性别、肿瘤大小及术前检验指标,根据术后病理是否存在MVI分为MVI阳性组和MVI阴性组。在训练集中利用Boruta算法和LASSO回归筛选出2组间与结局变量相关的指标,随后应用多因素逻辑回归、决策树(DT)、随机森林(RF)、极度梯度提升(XGboost)、K近邻(KNN)、支持向量机(SVM)、轻量梯度提升(LGBM)和朴素贝叶斯(Naive Bayes)等8种机器学习算法构建模型,在训练集和内部验证集中通过绘制受试者工作特征(ROC)曲线,筛选出曲线下面积(AUC)最佳的模型,并在外部验证集中进一步验证预测效能。通过校准曲线和临床决策曲线分别评估模型预测值与真实值的符合程度及模型的临床受益程度。结果经过筛选最终纳入肿瘤大小、甲胎蛋白、异常凝血酶原、嗜酸性粒细胞数、中性粒细胞数、肌酐、载脂蛋白A1、总胆红素共8个指标用于构建HCC术前MVI预测模型。在应用8种机器学习方法在训练集中构建模型时,XGboost算法构建的模型预测MVI的AUC为0.820,内部验证集中AUC为0.803,外部验证集中为0.758。此外,XGboost模型预测乙型肝炎表面抗原阳性HCC患者术前MVI的AUC为0.817,预测男性、高龄患者术前MVI的AUC分别为0.779和0.790。校准曲线可见预概率值曲线与实际发生概率曲线较为接近,临床决策曲线说明其在0.3~0.8的阈值概率范围内应用,可获得净收益。结论基于XGboost方法联合8个检验指标构建了HCC患者术前MVI预测模型,具有较好的预ObjectiveTo develop and validate a machine learning(ML)noninvasive model based on routine laboratory parameters to preoperatively predict the microvascular invasion(MVI)in patients with hepatocellular carcinoma(HCC).MethodsA total of 629 HCC patients who underwent hepatectomy at the First Affiliated Hospital of Fujian Medical University between January 2019 and December 2023 were retrospectively enrolled in this study and were divided chronologically into a training set(n=464)and internal validation set(n=165).A cohort with 190 HCC patients from Fujian Provincial Hospital were used as an external validation set.Preoperatively demographic features,tumor size and routine laboratory data were collected.All patients were divided into MVI-positive or MVI-negative group.The Boruta algorithm and LASSO regression algorithm were used to screen out related features in the training set.Eight different ML algorithms including multivariate logistic regression,decision tree(DT),random forest(RF),extreme gradient boosting(XGboost),k-nearest neighbor(KNN),support vector machine(SVM),light gradient boosting machine(LGBM)and Naive Bayes were used to construct the prediction models.The predictive performances of these models on training and internal validation sets were evaluated by the receiver operating characteristic(ROC)curve with the area under the curve(AUC).The ML model with the highest AUC values was defined as the optimal model and its performance was further validated in the external validation set.The calibration curve showed that the probability value curve was close to the actual occurrence probability curve,and the DCA showed that it could be applied within the threshold probability range of 0.3-0.8 to obtain net benefits.ResultsAfter screening,eight parameters includingα-fetoprotein(AFP),protein induced by vitamin K absenceⅡ(PIVKA-Ⅱ),tumor size,eosinophil count,neutrophil count,creatinine,ApoA1 and total bilirubin were finally selected for the construction of the preoperative prediction model for MVI in HCC.Amon

关 键 词：肝肿瘤 肝细胞癌 微血管浸润 预测模型 极度梯度提升 机器学习 

分 类 号：R735.7[医药卫生—肿瘤]