Predictive value of intratumoral-peritumoral habitat radiomics based on contrast-enhanced CT for microvascular invasion in hepatocellular carcinoma

TANG Quanquan; GAO Ruizhi; LI Jingdan; WEN Dongyue; WEI Yuchen; LUO Chaotian; HE Yun; YANG Hong

doi:10.19300/j.2026.L22092

Abstract：Objective To investigate the value of an intratumoral-peritumoral habitat radiomics model based on contrast-enhanced CT for the preoperative prediction of microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A total of 158 patients with pathologically confirmed HCC and known MVI status were retrospectively included.Clinical data and CT imaging features from arterial-phase and portal venous-phase contrast-enhanced upper abdominal scans were analyzed.The patients were randomly divided into a training set(n=110)and a testing set(n=48)at a ratio of 7∶3.Radiomics features were extracted from intratumoral,intratumoral-peritumoral,and intratumoral-peritumoral habitat subregions.Optimal features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.Univariate and multivariate logistic regression analysis were performed on clinical data to identify independent risk factors for predicting MVI.Models were constructed using the support vector machine(SVM)machine learning algorithm.Four models were developed using the training set:an intratumoral radiomics model,an intratumoral-peritumoral radiomics model,an intratumoral-peritumoral habitat radiomics model,and a clinical model.Model performance was evaluated in the testing set using the receiver operating characteristic(ROC)curve,the area under the curve(AUC),and net reclassification improvement(NRI).Calibration curves and decision curve analysis(DCA)were used to assess model calibration and clinical net benefit.Results Among the four constructed models,the intratumoral-peritumoral habitat radiomics model demonstrated the best predictive performance,with AUCs of 0.893 in the training set and 0.831 in the testing set.Compared with the intratumoral radiomics model,intratumoral-peritumoral radiomics model,and clinical model,the habitat radiomics model showed positive improvement in predictive ability(NRI>0).Calibration curves indicated good agreement between the predicted probabilities and the actual outcomes for the habitat radiomics model.Furthermore,compared with the clinical model,intratumoral radiomics model,and intratumoral-peritumoral radiomics model,the habitat radiomics model provided greater clinical net benefit.Conclusion The intratumoral-peritumoral habitat radiomics model based on contrast-enhanced CT exhibits excellent performance in predicting MVI in HCC patients.It has potential for noninvasive preoperative prediction of MVI and may provide a basis for personalized clinical treatment planning.