Abstract #0290
Robustness analysis of QSM radiomic features in patients with multiple sclerosis
Cristiana Fiscone1, Leonardo Rundo2, Alessandra Lugaresi1,3, David Neil Manners1, Kieren Allinson4, Elisa Baldin5, Raffaele Lodi1,6, Caterina Tonon1,6, Claudia Testa6,7, Mauro Castelli8, and Fulvio Zaccagna9,10,11
1Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy, 2Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Salerno, Italy, 3UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy, 4Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, 5Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy, 6Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy, 7Department of Physics and Astronomy, University of Bologna, Bologna, Italy, 8NOVA Information Management School, Universidade NOVA de Lisboa, Lisbon, Portugal, 9Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, 10Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 11Radcliffe Department of Medicine, Investigative Medicine Division, University of Oxford, Oxford, United Kingdom
Synopsis
This work evaluates the reliability of radiomic features in healthy controls and patients with multiple sclerosis using MR Quantitative Susceptibility Mapping (QSM), to guide the identification of future potential biomarkers. To ensure reproducibility, we assessed the non-lesioned parenchyma. Feature robustness was evaluated against the number of grey levels and echo times, measuring the Intraclass Correlation Coefficient (ICC). More than 65% of features were robust; different outcomes between regions were interpreted considering their anatomical characteristics (e.g. fibres’ orientation), confirmed by radiomic measurements (e.g. entropy). In future work, we are going to assess characterization and classification potential of those measurements.
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