The efficacy of ¹⁸F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

ABSTRACT:

Objective

To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (¹⁸F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs).

Methods

This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic ¹⁸F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUV_max), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted f

SUBMITTER: Nakajo M 

PROVIDER: S-EPMC10996420 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature