Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study.

ABSTRACT:

Importance

Intra-arterial therapies(IATs) are promising options for unresectable hepatocellular carcinoma(HCC). Stratifying the prognostic risk before administering IAT is important for clinical decision-making and for designing future clinical trials.

Objective

To develop and validate a machine learning(ML)-based decision support model(MLDSM) for recommending IAT modalities for unresectable HCC.

Design, setting, and participants

Between October 2014 and October 2022, a total of 2,959 patients with HCC who underwent initial IATs were enroled retrospectively from 13 tertiary hospitals. These patients were divided into the training cohort (n = 1700), validation cohort (n = 428), and test cohort (n = 200).

Main outcomes and measures

Thirty-two clinical variables w

SUBMITTER: An C 

PROVIDER: S-EPMC11369107 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature