Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma:Machine learning model based on contrast-enhanced computed tomography  

作　　者：Chao Zhang Hai Zhong Fang Zhao Zhen-Yu Ma Zheng-Jun Dai Guo-Dong Pang 

机构地区：[1]Department of Radiology,The Second Hospital of Shandong University,Jinan 250033,Shandong Province,China [2]Department of Radiology,Qilu Hospital of Shandong University,Jinan 250014,Shandong Province,China [3]Department of Radiology,Linglong Yingcheng Hospital,Yantai 265499,Shandong Province,China [4]Department of Scientific Research,Huiying Medical Technology Co.,Ltd,Beijing 100192,China

出　　处：《World Journal of Gastrointestinal Oncology》2024年第3期857-874,共18页世界胃肠肿瘤学杂志（英文版）（电子版）

基　　金：The study was reviewed and approved by the Second Hospital of Shandong University Institutional Review Board,IRB No.KYLL-2023LW044.

摘　　要：BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AU

关 键 词：Hepatocellular carcinoma Vessels encapsulating tumor clusters Intratumoral and peritumoral regions Radiomics features Nomog 

分 类 号：R735.7[医药卫生—肿瘤]