Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.

ABSTRACT:

Background

Hybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to investigate the effect of recently proposed fuzzy radiomics and compare its predictive performance to conventional radiomics in cancer imaging cohorts. In addition, lesion vs. lesion+surrounding fuzzy and conventional radiomic analysis was conducted.

Methods

Previously published 11C Methionine (MET) positron emission tomography (PET) glioma, 18F-FDG PET/computed tomography (CT) lung, and 68GA-PSMA-11 PET/magneto-resonance imaging (MRI) prostate cancer retrospective cohorts were included

SUBMITTER: Grahovac M 

PROVIDER: S-EPMC10119059 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature