Predicting microvascular invasion plus cytokeratin 19 expression positivity in hepatocellular carcinoma based on EOB-MRI using multitask deep learning

Aim: To construct and validate a multitask deep learning (DL) model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) contrast-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) plus cytokeratin 19 (CK19) positivity in patients with hepatocellular carcinoma (HCC). Methods: A total of 145 pathologically confirmed HCC patients undergoing preoperative enhanced MRI were assessed between January 2012 and January 2023. A predictive model, whose skeleton structure was an expert shared network based on spatial transformations and relational reasoning, was established based on hepatobiliary phase (HBP) images using a training set (n = 66, Center 1) and validated using an external (n = 79, Centers 2 and 3) test set. A receiver operating characteristic (ROC) curve was used to evaluate MVI CK19 positivity. Results: The area under the ROC curve (AUC) of the new model, named Expert Sharing Network, for the prediction of the CK19 and MVI expression was 0.87 and 0.88 in the training set and 0.80 and 0.85 in the validation set, respectively, which was superior to the ResNeSt50-based model, EfficientNet-b0-based model, and ResNet50-based model. The AUC of the DL model for the prediction of the MVI was 0.88 in the training set and 0.85 in the validation set, which was superior to the other three models. Conclusion: This new model can accurately predict CK19 expression and MVI in patients with HCC.