A Deep Learning Nomogram Based on Gd-EOB-DTPA MRI for Predicting Early Recurrence in Hepatocellular Carcinoma after Hepatectomy

Prognostic risk assessment after hepatectomy for patients with hepatocellular carcinoma (HCC) remains difficult. Previous studies have shown that Gd-EOB-DTPA MRI is sensitive and accurate for HCC detection, but studies in predicting early recurrence after hepatectomy based on deep learning (DL) are still lacking. This study investigated the performance of a Gd-EOB-DTPA MRI-based DL approach, and then evaluated the DL nomogram incorporating deep features and significant clinical indicators. DL nomogram outperformed the clinical nomogram (validation AUC: 0.909 vs. 0.715). The proposed DL nomogram could provide a noninvasive and comprehensive tool for predicting early recurrence of HCC after curative resection.