Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.

ABSTRACT:

Background

Pathological images of hepatocellular carcinoma (HCC) contain abundant tumor information that can be used to stratify patients. However, the links between histology images and the treatment response have not been fully unveiled.

Methods

We trained and evaluated a model by predicting the prognosis of 287 non-treated HCC patients postoperatively, and further explored the model's treatment response predictive ability in 79 sorafenib-treated patients. Based on prognostic relevant pathological signatures (PPS) extracted from CNN-SASM, which was trained by denoised recurrence label (DRL) under different thresholds, the PPS-based prognostic model was formulated. A total of 78 HCC patients from TCGA-LIHC were used for the external validation.

Results

We proposed th

SUBMITTER: Li Y 

PROVIDER: S-EPMC11921616 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature