Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis.

ABSTRACT:

Objectives

To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG).

Methods

Patients with histologically confirmed TET in the years 2000-2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model trainin

SUBMITTER: Bluthgen C 

PROVIDER: S-EPMC8687592 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature