A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [<sup>18</sup>F]FDG-PET Images During Follow-Up

A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [<sup>18</sup>F]FDG-PET Images During Follow-Up

<b>Objective</b>: To investigate the feasibility of a machine learning (ML) model based on radiomic features to identify active giant cell arteritis (GCA) in the aorta and differentiate it from atherosclerosis in follow-up [<sup>18</sup>F]FDG-PET/CT images for therapy monitor...

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Bibliographic Details
Main Authors: Hanne S. Vries, Gijs D. van Praagh, Pieter H. Nienhuis, Lejla Alic, Riemer H. J. A. Slart
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Diagnostics
Subjects:
giant cell arteritis
atherosclerosis
[<sup>18</sup>F]FDG PET
therapy monitoring
radiomics
Online Access:https://www.mdpi.com/2075-4418/15/3/367
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