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 

作者机构：Department of RadiologyThe Second Hospital of Shandong UniversityJinan 250033Shandong ProvinceChina Department of RadiologyQilu Hospital of Shandong UniversityJinan 250014Shandong ProvinceChina Department of RadiologyLinglong Yingcheng HospitalYantai 265499Shandong ProvinceChina Department of Scientific ResearchHuiying Medical Technology Co.LtdBeijing 100192China 

出 版 物：《World Journal of Gastrointestinal Oncology》 (世界胃肠肿瘤学杂志（英文版）（电子版）)

年 卷 期：2024年第16卷第3期

页      面：857-874页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

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

主　　题：Hepatocellular carcinoma Vessels encapsulating tumor clusters Intratumoral and peritumoral regions Radiomics features Nomog 

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 *** We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical *** analysis was conducted on intratumoral and peritumoral regions in the portal vein *** features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training *** model’s performance was validated on two separate test *** 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 *** most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological *** and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics *** 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 *** nomogram,combining clinicalradiological and combined radiomics features could acc