Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma

BackgroundMicrovascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics. PurposeTo 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 (50 cases) 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 and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029). ConclusionThe 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.

Automated summary

The purpose of this study was to develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors. The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, and the combined application of the two models had a high predictive efficacy.