增强CT检查预测肝细胞癌微血管侵犯的影像基因组学研究  

Radiogenomics of enhanced cT imaging to predict microvascular invasion in hepatocellular carcinoma

作　　者：赵建新 潘妮妮 何迪梁 施柳言 何炫明 熊恋秋 马丽丽 崔雅琼 赵莲萍[2] 黄刚[2] Zhao Jianxin;Pan Nini;He Diliang;Shi Liuyan;He Xuanming;Xiong Lianqiu;Ma Lili;Cui Yaqiong;Zhao Lianping;Huang Gang(First Clinical Medical College,Gansu University of Chinese Medicine,Lanzhou 730099,China;Department of Radiology,Gansu Provincial Hospital,Lanzhou 730099,China)

机构地区：[1]甘肃中医药大学第一临床医学院,兰州730099 [2]甘肃省人民医院放射科,兰州730099

出　　处：《中华消化外科杂志》2023年第11期1367-1377,共11页Chinese Journal of Digestive Surgery

基　　金：甘肃省人民医院院内科研基金(22GSSYD-34)。

摘　　要：目的构建基于术前增强CT检查的联合影像组学模型,预测肝细胞癌微血管侵犯(MVI)状态,对影像组学模型进行生物学解释.方法采用回顾性队列研究方法.收集癌症基因组图谱数据库建库至2023年1月纳入的424例肝细胞癌患者的mRNA数据,癌症图像档案馆数据库建库至2023年1月纳入的39例肝细胞癌患者和甘肃省人民医院2020年1月至2023年1月收治53例肝细胞癌患者的临床病理资料.92例肝细胞癌患者通过随机数字表法按7∶3分为训练集64例和测试集28例.分析动脉期及门静脉期CT检查图像及临床资料.使用3Dslicer软件(5.0.3版本)进行动脉期和门静脉期图像配准和三维感兴趣区勾画.使用开源软件FAE(0.5.5版本)对原始图像进行预处理并提取特征.通过最小绝对收缩和选择算子等方法筛选特征,构建影像组学模型并计算影像组学评分(R-score),通过Logistic回归整合临床参数、影像学特征及R-score构建列线图.通过加权基因共表达网络分析和相关性分析获取影像组学模型相关的基因模块并进行富集分析.观察指标:(1)不同MVI性质患者的临床特征比较.(2)MVI风险模型的建立.(3)MVI风险模型的评估.(4)基因模块聚类.(5)特征相关基因模块功能富集.正态分布的计量资料以(x)±s表示,组间比较采用独立样本t检验,偏态分布的计量资料以M(范围)表示,组间比较采用Mann-Whitney U检验,计数资料比较采用χ^(2)检验.采用组内和组间相关系数(ICC)评估影像组学特征提取的观察者间的一致性.ICC>0.75表示特征提取的一致性良好.单因素和多因素分析采用Logistic回归模型.绘制受试者工作特征曲线,以曲线下面积(AUC)、决策曲线、校准曲线评估模型的诊断效能及临床实用性.结果(1)不同MVI性质患者的临床特征比较.92例肝细胞癌患者中,MVI阳性47例,MVI阴性45例,两者肝炎、肿瘤长径、瘤周增强、瘤内动脉、假包膜及瘤周不光滑�Objective To construct a combined radiomics model based on preoperative enhanced computed tomography(CT)examination for predicting microvascular invasion(MVI)in hepatocellular carcinoma(HCC),and provide biological explanations for the radiomics model.Methods The retrospective cohort study was conducted.The messenger RNA(mRNA)of 424 HCC patients,the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023,and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People's Hospital from January 2020 to January 2023 were collected.The 92 HcC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7:3 based on a random number table method.The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed.The 3Dslicer software(version 5.0.3)was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest.The original images were preprocessed and the corresponding features were extracted by the open-source software FAE(version 0.5.5).After selecting features using the Least Absolute Shrinkage and Selection Operator,the radiomics model was constructed and the radiomics score(R-score)was calculated.The nomogram was constructed by integrating clinical parameters,imaging features and R-score based on Logistic regression.The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis.Observation indicators:(1)comparison of clinical characteristics of patients with different MVI properties;(2)establishment of MVI risk model;(3)evaluation of MVI risk model;(4)clustering of gene modules;(5)functional enrichment of feature-correlated gene modules.Measurement data with normal distribution were represented as Mean+SD,and

关 键 词：肝肿瘤 微血管侵犯 影像组学 预测模型 生物学解释 

分 类 号：R735.7[医药卫生—肿瘤]