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Multicenter Study
. 2022 Jul 1;57(7):423-432.
doi: 10.1097/RLI.0000000000000854. Epub 2022 Jan 31.

A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging

Loredana Storelli  1 , Matteo Azzimonti, Mor Gueye, Carmen Vizzino  1 , Paolo Preziosa, Gioachino Tedeschi  2 , Nicola De Stefano  3 , Patrizia Pantano, Massimo Filippi, Maria A Rocca
Affiliations
Multicenter Study

A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging

Loredana Storelli et al. Invest Radiol. .

Abstract

Objectives: Magnetic resonance imaging (MRI) is an important tool for diagnosis and monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for predicting disease worsening is still being debated. The aim of this study was to propose a deep learning algorithm to predict disease worsening at 2 years of follow-up on a multicenter cohort of MS patients collected from the Italian Neuroimaging Network Initiative using baseline MRI, and compare it with 2 expert physicians.

Materials and methods: For 373 MS patients, baseline T2-weighted and T1-weighted brain MRI scans, as well as baseline and 2-year clinical and cognitive assessments, were collected from the Italian Neuroimaging Network Initiative repository. A deep learning architecture based on convolutional neural networks was implemented to predict: (1) clinical worsening (Expanded Disability Status Scale [EDSS]-based model), (2) cognitive deterioration (Symbol Digit Modalities Test [SDMT]-based model), or (3) both (EDSS + SDMT-based model). The method was tested on an independent data set and compared with the performance of 2 expert physicians.

Results: For the test set, the convolutional neural network model showed high predictive accuracy for clinical (83.3%) and cognitive (67.7%) worsening, although the highest accuracy was reached when training the algorithm using both EDSS and SDMT information (85.7%). Artificial intelligence classification performance exceeded that of 2 expert physicians (70% of accuracy for the human raters).

Conclusions: We developed a robust and accurate model for predicting clinical and cognitive worsening of MS patients after 2 years, based on conventional T2-weighted and T1-weighted brain MRI scans obtained at baseline. This algorithm may be valuable for supporting physicians in their clinical practice for the earlier identification of MS patients at risk of disease worsening.

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Conflict of interest statement

Conflicts of interest and sources of funding: L.S. declared the receipt of grants (FISM2019/BR/009) and contracts from FISM (Fondazione Italiana Sclerosi Multipla) within a fellowship program. M.A., M.G., and C.V. declared no conflicts of interest. P. Preziosa received speaker honoraria from Biogen Idec, Novartis, Bristol Myers Squibb, Genzyme, and ExceMED. He is supported by a senior research fellowship FISM (cod. 2019/BS/009) and financed or cofinanced with the “5 per mille” public funding. G.T. has received compensation for consulting services and/or speaking activities from Biogen, Novartis, Merck, Genzyme, Roche, Teva, and receives research support from Biogen Idec, Merck Serono, and FISM. N.D.S. has received honoraria from Schering, Biogen Idec, Teva, Novartis, Genzyme, and Merck Serono S.A. for consulting services, and speaking and travel support. He serves on advisory boards for Biogen Idec Merck Serono S.A. and Novartis. P. Pantano has received funding for travel from Novartis, Genzyme, and Bracco and speaker honoraria from Biogen. M.F. is editor-in-chief of the Journal of Neurology and associate editor of Human Brain Mapping; received compensation for consulting services and/or speaking activities from Almiral, Alexion, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck Serono, Novartis, Roche, Sanofi, Takeda, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, FISM, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). M.A.R. received speaker honoraria from Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva, and receives research support from the MS Society of Canada and FISM. This study was partially supported by FISM with a research fellowship (FISM2019/BR/009) and research grants (FISM2018/R/16, FISM2018/S/3), and financed or cofinanced with the “5 per mille” public funding.

References

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