CT-based deep learning radiomics model for preoperative prediction of microvascular invasion in intrahepatic cholangiocarcinoma and its clinical value: a two-center study

Abstract

Abstract Background To develop and validate a computed tomography (CT) image-based deep learning radiomics model (DLRAD) for preoperative prediction of MVI in ICC patients, and to validate its relationship with prognosis. Methods A total of 165 ICC patients were recruited from two centers for retrospective study. Based on the radiomics and deep learning features of arterial phase CT images, dozens of models were constructed and compared using four machine learning methods. The incremental value of different sizes of peritumoral regions to the model was also explored. The performance of the model was evaluated using the area under the curve (AUC), calibration curve and decision curve. Kaplan-Meier curve was used to analyze the relationship between the model prediction results and prognosis. Results According to the radiomics features in the intratumoral and 2mm peritumoral regions and the deep learning features in the tumor, the DLRAD model constructed by the LR method showed the best discrimination ability for MVI. The AUC of the internal validation cohort was 0.86. The AUC of the external validation cohort was 0.89. In addition, the MVI predicted by the model was significantly correlated with the overall survival rate of patients (P = 0.005), which was consistent with the actual situation. Conclusion The DLRAD model constructed by radiomics and deep learning technology can effectively predict MVI in ICC patients. This provides clinicians with a powerful tool to help them make more accurate treatment decisions.