Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning.

ABSTRACT:

Background

Due to heterogeneity of hepatocellular carcinoma (HCC), outcome assessment of HCC with transarterial chemoembolization (TACE) is challenging.

Methods

We built histologic-related scores to determine microvascular invasion (MVI) and Edmondson-Steiner grade by training CT radiomics features using machine learning classifiers in a cohort of 494 HCCs with hepatic resection. Meanwhile, we developed a deep learning (DL)-score for disease-specific survival by training CT imaging using DL networks in a cohort of 243 HCCs with TACE. Then, three newly built imaging hallmarks with clinicoradiologic factors were analyzed with a Cox-Proportional Hazard (Cox-PH) model.

Findings

In HCCs with hepatic resection, two imaging hallmarks resulted in areas under the curve (AUCs)

SUBMITTER: Liu QP 

PROVIDER: S-EPMC7284069 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature