Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning-based segmentation with radiomics-based histological grading using fused multi-phase MRI. MATERIALS AND METHODS: In this retrospective study, MRI data from 1673 patients with histopathologically confirmed hepatocellular carcinoma (875 high-grade, 798 low-grade) were analyzed. Arterial-phase and portal-venous-phase T1-weighted images were acquired using a standardized, bolus-tracking protocol to ensure consistent contrast timing. Six segmentation models, Vision Transformer, nnU-Net, U-Net, DeepLabV3+, Swin Transformer, and SegNet, were trained on arterial, portal-venous, and fused (wavelet-based) MRI data. Radiomic features (n = 215) were extracted from segmented tumor volumes and pre-filtered to remove multicollinearity. Feature refinement was performed using Lasso, Recursive Feature Elimination (RFE), and ANOVA. Tumor grade classification was conducted using TabTransformer, TabNet, XGBoost, and CatBoost. Five-fold cross-validation and an independent test set were used for robust evaluation. Standardized preprocessing, including intensity normalization, bias field correction, and inter-phase registration, ensured consistent image quality and analytical reproducibility. RESULTS: The proposed framework achieved high segmentation accuracy with DSC scores above 0.92 across fused MRI images. Classification performance was exceptional, with training accuracy reaching 93.2% and testing accuracy 92.5%, while AUC values exceeded 96% in the fused modality. Comparative analyses revealed that the Transformer-RFE-Fused model outperformed alternative architectures, demonstrating superior generalization and robust feature learning. In addition, SHAP analysis confirmed the high contribution of key radiomic features, and t-SNE visualizations illustrated clear separation between low-grade and high-grade tumors. These results validate the efficacy of our multi-task learning approach in enhancing HCC tumor segmentation and grading. Our evaluation underscores the clinical potential of our integrated framework for accurate, reproducible, and interpretable HCC diagnosis. CONCLUSIONS: Our integrated multi-task learning framework markedly improves HCC tumor segmentation and grading. Transformer-RFE-Fused (Wavelet) MRI offers superior accuracy and robustness, efficiently paving the way for enhanced clinical decision-making.