Transformer-Based Deep Learning for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Abstract Background Microvascular invasion (MVI) is a critical prognostic factor in hepatocellular carcinoma (HCC), but preoperative three-class prediction remains challenging. Radiomics and clinical biomarkers may enable more accurate and individualized assessment. Aim To develop and validate a Transformer-based deep learning framework that integrates radiomic and clinical features for direct three-class MVI classification in HCC patients. Methods This retrospective study included 438 patients with pathologically confirmed HCC and MVI status from a single institution. Radiomic features were extracted from preoperative Gd-BOPTA-enhanced MRI, and clinical laboratory data were collected. A two-stage feature selection strategy, combining univariate statistical testing and recursive feature elimination, was applied. A Transformer-based model was built to classify three MVI categories (M0, M1, M2), and its performance was evaluated on internal and external test sets. Results were compared with traditional machine learning models, including Random Forest, Logistic Regression, XGBoost, and LightGBM. Results The Transformer-based model achieved an accuracy of 0.733, a weighted F1-score of 0.733, and a macro-average AUC of 0.880 (95% CI: 0.807–0.953) on the internal test set. On the external validation set, it reached an accuracy of 0.758, a weighted F1-score of 0.768, and a macro-average AUC of 0.886 (95% CI: 0.833–0.940). It outperformed traditional classifiers and showed superior ability to identify high-risk M2 cases. Conclusions This Transformer-based model enables accurate and objective three-class MVI prediction using multimodal features, supporting individualized surgical planning and improved clinical outcomes.