Preoperative ¹⁸F-FDG PET/CT radiomics analysis for predicting HER2 expression and prognosis in gastric cancer.

ABSTRACT:

Background

We aimed to establish and validate 2 machine learning models using ¹⁸F-flurodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic features to predict human epidermal growth factor receptor 2 (HER2) expression and prognosis in gastric cancer (GC) patients.

Methods

We retrospectively enrolled 90 patients diagnosed with GC, including their clinical information and the ¹⁸F-FDG PET/CT images. Patients were allocated to a training cohort of 72 patients and an independent validation cohort (IVC) of 18 patients. There were 2,100 radiomic features extracted from the ¹⁸F-FDG PET/CT scans. A sequential combination of multivariate and univariate feature selection was applied, including sequential fo

SUBMITTER: Liu Q 

PROVIDER: S-EPMC10006101 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature