Predictive factors of microvascular invasion in patients with intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance images.

Purpose: The aim of this retrospective study was to develop and validate a preoperative nomogram for predicting microvascular invasion (MVI) in patients with intrahepatic mass-forming cholangiocarcinoma (IMCC) based on magnetic resonance imaging (MRI). Methods: In this retrospective study, 224 consecutive patients with clinicopathologically confirmed IMCC were enrolled. Patients whose data were collected from February 2010 to December 2020 were randomly divided into the training (131 patients) and internal validation (51 patients) datasets. The data from January 2021 to November 2021 (42 patients) were allocated to the time-independent validation dataset. Univariate and multivariate forward logistic regression analyses were used to identify preoperative MRI features that were significantly related to MVI, which were then used to develop the nomogram. We used the area under the receiver operating characteristic curve (AUC) and calibration curve to evaluate the performance of the nomogram. Results: Interobserver agreement of MRI qualitative features was good to excellent, with κ values of 0.613–0.882. Multivariate analyses indicated that the following variables were independent predictors of MVI: multiple tumours (odds ratio [OR]) = 4.819, 95% confidence interval [CI] 1.562–14.864, P = 0.006), ill-defined margin (OR = 6.922, 95% CI 2.883–16.633, P < 0.001), and carbohydrate antigen 19–9 (CA 19–9) > 37 U/ml (OR = 2.890, 95% CI 1.211–6.897, P = 0.017). A nomogram incorporating these factors was established using well-fitted calibration curves. The nomogram showed good diagnostic efficacy for MVI, with AUC values of 0.838, 0.819, and 0.874 for the training, internal validation, and time-independent validation datasets, respectively. Conclusion: A nomogram constructed using independent factors, namely the presence of multiple tumours, ill-defined margins, and CA 19–9 > 37 U/ml could predict the presence of MVI. This can facilitate personalised therapeutic strategy and clinical management in patients with IMCC. [ABSTRACT FROM AUTHOR]