Radiomics with Clinical Data and [<sup>18</sup>F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study

Radiomics with Clinical Data and [<sup>18</sup>F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study

<b>Objective:</b> We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [<sup>18</sup>F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). <b>Me...

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Bibliographic Details
Main Authors: Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha, Ben R. Saleem
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Diagnostics
Subjects:
intracavitary vascular (endo)graft infections
radiomics
machine learning
[<sup>18</sup>F]FDG PET
MAGIC criteria
Online Access:https://www.mdpi.com/2075-4418/15/15/1944
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