Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning.

ABSTRACT:

Background

Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment.

Methods

A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological scor

SUBMITTER: Qu WF 

PROVIDER: S-EPMC9439660 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature