To investigate the prediction value of CT-based radiomics model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Clinical data of 129 patients who underwent surgical resection in the Affiliated Hospital of Kunming University of Science and Technology from 2015 to 2022 were retrospectively analyzed. Among them, 108 patients were male and 21 female, aged from 25 to 83 years, with a median age of 52 years. According to the 3: 1 ratio using random number table method, all cases were divided into the training (n=96) and test sets (n=33). All patients received enhanced CT scan within preoperative 1 month. Through the artificial intelligence big-data analysis platform of imaging radiomics, Lasso algorithm was used to screen the optimal features from clinical and imaging radiomic features. Using enhanced CT images, a single clinical model (C model) and a single imaging radiomic model (R model) were constructed based on preoperative CT scan of tumors and peritumoral 0-1, 1-2 and 2-3 cm. According to the weighted coefficient corresponding to the optimal features, the radiomic score (Rad-score) of each model was obtained, and the area under the ROC curve (AUC) of each model was calculated according to the Rad-score. The prediction capability of the model was evaluated by the consistency index (C-index). The higher the index, the higher the prediction ability.

Lasso algorithm was employed to screen 3, 9, 15 and 50 optimal features of tumor and peritumoral 0-1, 1-2 and 2-3 cm from 1818 clinical and imaging radiomic features. In the training set, the penalty parameters were optimized by 5-fold cross-validation, and Lasso-Logistic regression model was constructed. In the test set, R model could better predict the risk of MVI than C model (AUC=0.883, 0.848, 0.800, 0.848 and 0.500, 0.704, 0.500 and 0.639). High consistency was found between the risk estimated by 4 R models in the training and test sets and the actual risk of MVI, indicating R models yielded good predictive ability (C-index=0.746 and 0.883, 0.738 and 0.848, 0.732 and 0.800, 0.672 and 0.848) and favorable correction performance.

In this study, R models of tumor and peritumoral 0-1, 1-2 and 2-3 cm are established based on preoperative CT scan. R models perform better than C models. Rad-score transformed by R models can be used as an independent predictor of MVI.