Predictive Value Of Radiomic Analysis Of Enhanced CT Combined With Clinical Risk Factors For Three-tiered Grading Of Microvascular Invasion In Hepatocellular Carcinoma

ObjectiveTo evaluate the value of enhanced CT in preoperative prediction of microvascular invasion and grading of hepatocellular carcinoma.Methods167 cases of hepatocellular carcinoma confirmed by surgical pathology were retrospectively analyzed,and the data were divided into the training group(118 cases)and the test group(49 cases)according to 7:3.In the training group,68 cases were MVI positive(including 41 cases in the low-risk group and 27 cases in the high-risk group),and 50 cases were MVI negative.The data collected in the training group were analyzed by single factor analysis and recursive feature elimination to obtain the important risk factors affecting the MVI of HCC patients,and the clinical imaging model was established,and the diagnostic efficacy of the model was verified by the test group.ResultsThe clinical imaging features of MVI included aspartate aminotransferase,platelet and lymphocyte ratio,tumor maximum diameter,tumor margin,peritumoral enhancement,intratumoral artery,and peritumoral hypodensity.The diagnostic efficiency AUC values of clinical imaging model for M0,M1 and M2 were 0.87,0.68 and 0.88,respectively,and the AUC values of test group were 0.81,0.59 and0.79,respectively.ConclusionsThe clinical-imaging model based on enhanced CT has certain clinical application value in noninvasive assessment and prediction of the occurrence and grading of MVI.ObjectiveTo develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.MethodsThe data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively.The patients were divided into the training(116 cases)and test(50cases)groups at a ratio of 7:3.Of them,69 cases were MVI positive in the training group,including 45 cases in the low-risk group(M1)and 24 cases in the high-risk group(M2),and 47 cases were MVI negative(M0).In the training group,the optimal subset features were obtained through feature selection,and the arterial phase radiomics model,portal venous phase radiomics model,delayed phase radiomics model,three-phase radiomics model,clinical imaging model,and combined model were developed using Linear Support Vector Classification.The test group was used for validation,and the efficacy of each model was evaluated through the receiver operating characteristic curve(ROC).ResultsThe clinical imaging features of MVI included alpha-fetoprotein,tumor size,tumor margin,peritumoral enhancement,intratumoral artery,and low-density halo.The area under the curve(AUC)of the ROC values of the clinical imaging model for M0,M1,and M2 were 0.831,0.701,and 0.847,respectively,in the training group and0.782,0.534,and 0.785,respectively,in the test group.After combined radiomics analysis,the AUC values for M0,M1,and M2 in the test group were 0.818,0.688,and0.867,respectively.The difference between the clinical imaging model and the combined model was statistically significant(P=0.029).ConclusionsThe clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading,which can provide precise reference value for preoperative clinical diagnosis and treatment.The combined application of the two models had a high predictive efficacy.