Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation.

ABSTRACT:

Background and purpose

Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning.

Patients and methods

Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis accord

SUBMITTER: Qu WF 

PROVIDER: S-EPMC10386986 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature