Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.

ABSTRACT:

Background

Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC).

Methods

A total of 1404 primary tumour radiomic features were extracted from pre-treatment [¹⁸F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospe

SUBMITTER: Oliveira C 

PROVIDER: S-EPMC8380219 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature