Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease

PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inve...

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Bibliographic Details
Main Authors: Giulia Colelli, Leonardo Barzaghi, Matteo Paoletti, Mauro Monforte, Niels Bergsland, Giulia Manco, Xeni Deligianni, Francesco Santini, Enzo Ricci, Giorgio Tasca, Antonietta Mira, Silvia Figini, Anna Pichiecchio
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-02-01
Series:Frontiers in Neurology
Subjects:
radiomics
machine learning
muscle MRI
stir
FSHD
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2023.1105276/full

Internet

https://www.frontiersin.org/articles/10.3389/fneur.2023.1105276/full

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