A Bayesian Network Prediction Model for Microvascular Invasion in Patients with Intrahepatic Cholangiocarcinoma: A Multi-institutional Study

Background: Microvascular invasion (MVI) has been reported to be an independent prognostic factor of recurrence and poor overall survival in patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the preoperative independent risk factors of MVI and establish a Bayesian network (BN) prediction model to provide a reference for surgical diagnosis and treatment. Methods: A total of 531 patients with ICC who underwent radical resection between 2010 and 2018 were used to establish and validate a BN model for MVI. The BN model was established based on the preoperative independent variables. The ROC curves and confusion matrix were used to assess the performance of the model. Results: MVI was an independent risk factor for relapse-free survival (RFS) (P < 0.05). MVI has a correlation with postoperative recurrence, early recurrence (< 6 months), median RFS and median overall survival (all P < 0.05). The preoperative independent risk variables of MVI included obstructive jaundice, prognostic nutritional index, CA19-9, tumor size, and major vascular invasion, which were used to establish the BN model. The AUC of the BN model was 78.92% and 83.01%, and the accuracy was 70.85% and 77.06% in the training set and testing set, respectively. Conclusion: The BN model established based on five independent risk variables for MVI is an effective and practical model for predicting MVI in patients with ICC.