A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.

Accurate prediction of recurrence risk after hepatectomy still remains a clinical challenge for hepatocellular carcinoma (HCC). We develop a deep learning radiopathomic (DLRP) signature to fuse deep features of CT images and histological whole-slide-image, aiming to predict the recurrence-free survival (RFS) in HCC patients. The multi-omics data in The Cancer Genome Atlas (TCGA) database are used to assess the potential biological interpretation. A total of 599 patients are enrolled in this study and divided into the training (n = 272), internal test (n = 120), external test (n = 174), and TCGA (n = 33) cohorts. The DLRP signature shows better prediction for RFS than radiomics signature, pathomics signature, clinical model, and Barcelona Clinic Liver Cancer stage in the external test cohort (C-index, 0.799 vs 0.541-0.738; P value range, <0.001-0.042). Patients in the high-risk group show worse RFS and overall survival in three cohorts than those in the low-risk group (all P < 0.001). The multi-omics data indicate that DLRP signature is relevant to Wnt/β-catenin signaling pathway and tumor immune infiltration. We conclude that the DLRP signature can efficiently predict recurrence risk in HCC patients, thereby facilitating personalized precision therapy.