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.

Fiscone, C., Rundo, L., Lugaresi, A., Manners, D.N., Allinson, K., Baldin, E., et al. (2023). Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. SCIENTIFIC REPORTS, 13, 1-16 [10.1038/s41598-023-42914-4].

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

Fiscone, Cristiana;Lugaresi, Alessandra;Manners, David Neil;Baldin, Elisa;Vornetti, Gianfranco;Lodi, Raffaele;Tonon, Caterina;Testa, Claudia;Zaccagna, Fulvio
2023

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.
2023
Fiscone, C., Rundo, L., Lugaresi, A., Manners, D.N., Allinson, K., Baldin, E., et al. (2023). Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. SCIENTIFIC REPORTS, 13, 1-16 [10.1038/s41598-023-42914-4].
Fiscone, Cristiana; Rundo, Leonardo; Lugaresi, Alessandra; Manners, David Neil; Allinson, Kieren; Baldin, Elisa; Vornetti, Gianfranco; Lodi, Raffaele;...espandi
File in questo prodotto:
File Dimensione Formato  
Fiscone_2023.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 3.5 MB
Formato Adobe PDF
3.5 MB Adobe PDF Visualizza/Apri
suppl_mat.pdf

accesso aperto

Tipo: File Supplementare
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 1.25 MB
Formato Adobe PDF
1.25 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/959448
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact