Preoperative MVI prediction in intrahepatic cholangiocarcinoma via deep learning analysis of intratumoral and peritumoral features on multi-sequence MRI.

backgroundTo explore the prediction effectiveness of intratumoral and peritumoral regions in multi-sequence magnetic resonance imaging (MRI) based on deep learning（DL）, determine the optimal region of interest (ROI), and develop an efficient preoperative prediction model for microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC).

methodsClinical, pathological, and preoperative MRI data of 200 patients with ICC confirmed by surgical pathology were retrospectively collected. All patients had preoperative contrast-enhanced MRI scans. Patients were randomized in a 7:3 ratio into a training cohort (n = 140) and a validation cohort (n = 60). Univariate and multivariate logistic regression were used to screen for independent clinical imaging risk factors for MVI. Deep learning features were extracted from three different ROIs (ROItumor, ROIperi10mm, and ROItumor+peri10mm) of the maximum transverse cross-section of tumors in six MRI sequence images using a pre-trained ResNet-18. Construct clinical-imaging model, tumor model, peri10mm model, tumor+peri10mm model, and combined model with deep learning score and clinical-imaging features based on logistic regression (LR) classifiers for predicting MVI. Using the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC), the models’ effectiveness in predicting MVI was evaluated. The clinical utility of these models were evaluated using decision curve analysis (DCA). Interpretability analysis of the best predictive model using the Shapley additive explanation(SHAP) method.

resultsTumor size, intrahepatic duct dilatation, and arterial edge enhancement ratio were independent MVI predictors. Superior prediction performance was shown by the tumor+peri10mm model (training set AUC 0.932, validation set AUC 0.872). The model’s performance was further enhanced by the incorporation of clinical imaging features (AUC 0.883, sensitivity 84.2%, specificity 78.0%). And all of the differences with the other prediction models showed statistical significance, based on the DeLong test (all p＜0.05). SHAP-based explainability research highlighted the significance of deep learning score.

conclusionsIn summary, the combined model integrating clinical imaging features with deep learning features derived from multi-sequence MRI within the tumor and at a 10-millimeter depth around the tumor demonstrated favorable results in predicting the MVI status of ICC patients. This approach may aid in guiding relevant clinical decisions and improving patient prognosis. CLINICAL TRIAL NUMBER: Not applicable.