Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [¹⁸F]FDG PET-CT.

ABSTRACT: Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms. Methods: This retrospective study included 140 patients who underwent [¹⁸F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis

SUBMITTER: Flaus A 

PROVIDER: S-EPMC7928279 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature