Clinical-radiomics models with machine-learning algorithms to distinguish uncomplicated from complicated acute appendicitis in adults: a multiphase multicenter cohort study.

ABSTRACT: Increasing evidence suggests that non-operative management (NOM) with antibiotics could serve as a safe alternative to surgery for the treatment of uncomplicated acute appendicitis (AA). However, accurately differentiating between uncomplicated and complicated AA remains challenging. Our aim was to develop and validate machine-learning-based diagnostic models to differentiate uncomplicated from complicated AA. This was a multicenter cohort trial conducted from January 2021 and December 2022 across five tertiary hospitals. Three distinct diagnostic models were created, namely, the clinical-parameter-based model, the CT-radiomics-based model, and the clinical-radiomics-fused model. These models were developed using a comprehensive set of eight machine-learning algorithms, which included logi

SUBMITTER: Li L 

PROVIDER: S-EPMC12133283 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature