Correlated and individual feature learning with contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma

Abstract

Malignancy characterization of hepatocellular carcinoma (HCC) is of great importance in patient management and prognosis prediction. In this study, we propose an end-to-end correlated and individual feature learning framework to characterize the malignancy of HCC from Contrast-enhanced MR. From the phases of pre-contrast, arterial and portal venous, our framework simultaneously and explicitly learns both the shareable and phase-specific features that are discriminative to malignancy grades. We evaluate our method on the Contrast enhanced MR of 112 consecutive patients with 117 histologically proven HCCs. Experimental results demonstrate that arterial phase yields better results than portal vein and pre-contrast phase. Furthermore, phase specific components show better discriminant ability than the shareable components. Finally, combining the extracted shareable and individual features components has yielded significantly better performance than traditional feature fusion methods. We also conduct t-SNE analysis and feature scoring analysis to qualitatively assess the effectiveness of the proposed method for malignancy characterization.