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<p>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 (<i>n</i> = 272), internal test (<i>n</i> = 120), external test (<i>n</i> = 174), and TCGA (<i>n</i> = 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; <i>P</i> value range, &lt;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 <i>P</i> &lt; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Gongzheng Wang,
  • Wei Chen,
  • Zhuoshuai Liang,
  • Feier Ding,
  • Jin Cui,
  • Zijian Shen,
  • Fan Lin,
  • Xiujuan Li,
  • Ximing Wang,
  • Xinya Zhao

摘要

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.