Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study.

ABSTRACT:

Background

There is a growing need for new improved classifiers of prognosis in hepatocellular carcinoma (HCC) patients to stratify them effectively.

Methods

A deep learning model was developed on a total of 1118 patients from 4 independent cohorts. A nucleus map set (n = 120) was used to train U-net to capture the nuclear architecture. The training set (n = 552) included HCC patients that had been treated by resection. The liver transplantation (LT) set (n = 144) contained patients with HCC that had been treated by LT. The train set and its nuclear architectural information extracted by U-net were used to train the MobileNet V2-based classifier (MobileNetV2_HCC_class). The classifier was then independently tested on the LT set and externally validated on the TCGA set (n = 3

SUBMITTER: Liu Z 

PROVIDER: S-EPMC9174321 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature