Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical a...Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility 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 learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct 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 development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,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 validation(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 subsequent MaVI development in HCC with prognostic implications.展开更多
基金supported by grants from the National Key R&D Program of China(2017YFA0205200,2017YFC1308701,and 2017YFC1309100)National Natural Science Foundation of China(82001917,81930053,81227901,81771924,81501616,81571785,81771957,and 61671449)the Natural Science Foundation of Guangdong Province,China(2016A030311055 and 2016A030313770)。
文摘Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility 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 learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct 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 development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,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 validation(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 subsequent MaVI development in HCC with prognostic implications.
