A deep learning model for personalized intra-arterial therapy planning in unresectable hepatocellular carcinoma: a multicenter retrospective study.

ABSTRACT:

Background

Unresectable Hepatocellular Carcinoma (uHCC) poses a substantial global health challenge, demanding innovative prognostic and therapeutic planning tools for improved patient management. The predominant treatment strategies include Transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC).

Methods

Between January 2014 and November 2021, a total of 1725 uHCC patients [mean age, 52.8 ± 11.5 years; 1529 males] received preoperative CECT scans and were eligible for TACE or HAIC. Patients were assigned to one of the four cohorts according to their treatment, four transformer models (SELECTION) were trained and validated on each cohort; AUC was used to determine the prognostic performance of the trained models. Patients were stratified into

SUBMITTER: Lin X 

PROVIDER: S-EPMC11407998 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature