典型文献
CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study
文献摘要：
Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)pa-tients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possi-bility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learn-ing methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to con-struct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI develop-ment.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI develop-ment,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P＜0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external valida-tion(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P＜0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P＜0.001),PFS(P＜0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of sub-sequent MaVI development in HCC with prognostic implications.
文献关键词：
中图分类号：
作者姓名：
Jing-Wei Wei;Si-Rui Fu;Jie Zhang;Dong-Sheng Gu;Xiao-Qun Li;Xu-Dong Chen;Shuai-Tong Zhang;Xiao-Fei He;Jian-Feng Yan;Li-Gong Lu;Jie Tian
作者机构：
Key Laboratory of Molecular Imaging,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Beijing Key Laboratory of Molecular Imaging,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Zhuhai Interventional Medical Center,Zhuhai Precision Medical Center,Zhuhai People's Hospital,Zhuhai Hospital of Jinan University,Zhuhai 519000,China;Department of Radiology,Zhuhai Precision Medical Center,Zhuhai People's Hospital,Zhuhai Hospital of Jinan University,Zhuhai 519000,China;Department of Interventional Treatment,Zhongshan City People's Hospital,Zhongshan 528400,China;Department of Radiology,Shenzhen People's Hospital,Shenzhen 518000,China;Interventional Diagnosis and Treatment Department,Nanfang Hospital,Southern Medical University,Guangzhou,510000,China;Department of Radiology,Yangjiang People's Hospital,Yangjiang 529500,China;Beijing Advanced Innovation Center for Big Data-Based Precision Medicine,School of Medicine and Engineering,Beihang University,Beijing 100191,China;Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education,School of Life Science and Technology,Xidian University,Xi'an 710126,China
文献出处：
引用格式：
[1]Jing-Wei Wei;Si-Rui Fu;Jie Zhang;Dong-Sheng Gu;Xiao-Qun Li;Xu-Dong Chen;Shuai-Tong Zhang;Xiao-Fei He;Jian-Feng Yan;Li-Gong Lu;Jie Tian-.CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study)[J].国际肝胆胰疾病杂志(英文版),2022(04):325-333
A类：
Macrovascular,MaVI,manufactory,Peel9,InterquartileRange
B类：
radiomics,development,macrovascular,invasion,hepatocellular,carcinoma,multicenter,study,Background,occurs,nearly,half,HCC,diagnosis,during,follow,which,causes,severe,disease,deterioration,limits,possi,bility,surgical,approaches,This,aimed,investigate,whether,computed,tomography,analysis,could,help,Methods,cohort,patients,diagnosed,was,enrolled,from,hospitals,complete,prognosis,signature,built,via,strategy,machine,learn,methods,Afterwards,related,clinical,factors,were,integrated,struct,final,prediction,CRIM,random,forest,modeling,Cox,regression,used,independent,risk,Kaplan,Meier,conducted,stratify,according,progression,free,survival,PFS,overall,OS,selected,Results,showed,significant,improvement,compared,conventional,radiological,predictors,satisfactory,areas,under,curve,training,external,valida,datasets,respectively,presented,excellent,generalization,each,that,accepted,disparate,scanning,protocol,fos,hazard,cox,successfully,stratified,into,high,groups,regarding,Conclusions,quantitative,enable,accuracy,sub,sequent,prognostic,implications
AB值：
0.478501
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