Identification and validation of an ultrasound-based interpretable machine learning model for the preoperative evaluation of microvascular invasion in patients with hepatocellular carcinoma

The aim of our study was to develop and validate a machine learning model for the preoperative identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). This retrospective multicenter study was conducted in China. Patients with HCC from June 2017 to December 2024 were enrolled. Database was divided into training and internal validation sets randomly. Least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. Four machine learning algorithms were compared for MVI prediction. Model performance was evaluated using the area under the receiver operating characteristic (AUC), accuracy, sensitivity, specificity, precision, Youden's index, and F1 score. Finally, the machine learning model with the best performance was selected as our final model while using it for an independent external validation set. The SHapley Additive exPlanations (SHAP) diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features. The study finally enrolled 496 patients, comprising 229 MVI-positive and 267 MVI-negative cases. A total of 42 patients with HCC were collected in the independent external validation center, of which 18 were MVI-positive. LASSO regression showed that AFP, tumor size, peripheral enhancement, mosaic architecture and washout start time were the significant predictors. Among the four models, the Gradient Boosting Machine (GBM) model showed the best performance in the internal validation set, with an AUC of 0.829. In the independent external validation set, the GBM model demonstrated an AUC of 0.812. The machine learning model shows promising efficacy in preoperative MVI identification for HCC patients. This method has potential clinical applications and may help identify MVI preoperatively, potentially improving clinical outcomes.