Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma.

Abstract

Objectives: The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models.

Materials and methods: Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36).

Results: Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI.

Conclusions: CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.