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Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis

Authors :
Cristiana Fiscone
Leonardo Rundo
Alessandra Lugaresi
David Neil Manners
Kieren Allinson
Elisa Baldin
Gianfranco Vornetti
Raffaele Lodi
Caterina Tonon
Claudia Testa
Mauro Castelli
Fulvio Zaccagna
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.8a86b56aa3c341ffad9f0bc94b82e966
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-42914-4