Machine learning models from computed tomography to diagnose thymic epithelial tumors requiring combined resection.

ABSTRACT:

Background

Minimally invasive approaches have been a standard choice of surgery for noninvasive thymic epithelial tumors (TETs), but we sometimes experience cases requiring combined resection of adjacent structures. We develop and validate machine learning models to predict combined resection based on preoperative contrast-enhanced computed tomography (CT).

Methods

This study included 212 patients with TETs (140 in the training cohort and 72 in the validation cohort) who underwent radical surgery. Radiomics features were extracted from contrast-enhanced CT and predicted with five feature selection methods and seven machine learning models in nested cross validation. The clinical utility of the models was analyzed by a decision curve analysis (DCA).

Results

Fifty-five

SUBMITTER: Onozato Y 

PROVIDER: S-EPMC11388251 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature