Predicting microvascular invasion in hepatocellular carcinoma using contrast-enhanced CT-based deep learning models: a comparative study of transformer, CNN, and MIL

Objective Microvascular invasion (MVI) predicts postoperative recurrence in hepatocellular carcinoma (HCC), yet preoperative prediction remains challenging. This study compared a Transformer, a convolutional neural network (CNN), and a multiple instance learning (MIL) framework for predicting MVI from contrast-enhanced CT. Methods We retrospectively enrolled 236 patients with pathologically confirmed HCC. Tumor regions of interest were semi-automatically delineated on arterial-phase CT images, and slices centered on the largest tumor cross-section were sampled to build a 2.5D dataset. Four models—Transformer, MIL, CNN, and a clinical baseline—were trained. Models were compared using the area under the receiver operating characteristic curve (AUC) with the DeLong test; bootstrap resampling assessed the stability of AUC estimates. Results The Transformer achieved the highest AUC of 0.827 (95% CI 0.7585–0.8962), with an accuracy of 0.775, sensitivity of 0.577, and specificity of 0.889, followed by MIL (0.766; 95% CI 0.6875–0.8441), CNN (0.723), and the clinical model (0.715). Bootstrap validation confirmed stable AUC estimates for all models, with the Transformer ranking first (mean 0.827; SD 0.034). The Transformer significantly outperformed the CNN (adjusted P = 0.001) and the clinical model (adjusted P = 0.039); after correction for multiple comparisons, its difference from MIL was of borderline significance (adjusted P = 0.097). No significant differences were observed among the CNN, MIL, and clinical models. Conclusion Among the four models evaluated, the contrast-enhanced CT-based Transformer predicted MVI most accurately, and its advantage persisted after bootstrap internal validation. These findings concern the ranking of discriminative performance only; the clinical utility of the model is not established, and prospective validation in larger cohorts is required before any clinical application.