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Original article| Volume 74, 104708, June 2023

Therapy effect on AI-derived thalamic atrophy using clinical routine MRI protocol: A longitudinal, multi-center, propensity-matched multiple sclerosis study

  • Dejan Jakimovski
    Affiliations
    Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine, and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA
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  • Diego Silva
    Affiliations
    Bristol Myers Squibb, Summit, NJ, USA
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  • Niels Bergsland
    Affiliations
    Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine, and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA
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  • Michael G. Dwyer
    Affiliations
    Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine, and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA

    Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, USA
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  • Bianca Weinstock-Guttman
    Affiliations
    Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA
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  • Ralph HB. Benedict
    Affiliations
    Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA
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  • Jon Riolo
    Affiliations
    Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA
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  • Robert Zivadinov
    Correspondence
    Corresponding author at: Department of Neurology Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo 100 High St., Buffalo, NY 14203, USA.
    Affiliations
    Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine, and Biomedical Sciences, University at Buffalo, State University of New York, NY, USA

    Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, USA
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  • on behalf of theDeepGRAI Registry Study group

      Highlights

      • Study aimed at utilizing artificial intelligence (AI)-based volumetric analysis on routine unstandardized T2-FLAIR scans in a multi-center study of 1002 people with relapsing-remitting MS (pwRRMS).
      • After propensity matching untreated pwRRMS had significantly greater 2-year thalamic atrophy when compared to treated pwRRMS (−1.2% vs. −0.3%, p = 0.044).
      • PwRRMS treated with high-efficacy DMTs had two-fold lower central atrophy rate when compared to pwRRMS treated on moderate-efficacy DMTs (3.5% vs. 7.0%, p = 0.001).
      • Non-harmonized MRI scans acquired through routine MS care can be utilized for measuring the neuroprotective DMT effect in a real-world setting.

      Abstract

      Background

      The effect of disease modifying therapies (DMTs) on brain atrophy in persons with multiple sclerosis (pwMS) is typically investigated in highly standardized clinical trial settings or single-center academic institutions. We aimed at utilizing artificial intelligence (AI)-based volumetric analysis on routine unstandardized T2-FLAIR scans in determining the effect of DMTs on lateral ventricular volume (LVV) and thalamic volume (TV) changes in pwMS.

      Methods

      The DeepGRAI (Deep Gray Rating via Artificial Intelligence) registry is a multi-center, longitudinal, observational, real-word study with a convenience sample of 1002 relapsing-remitting (RR) pwMS from 30 United States sites. Brain MRI exams acquired as part of the routine clinical management were collected at baseline and on average at 2.6-years follow-up. The MRI scans were acquired either on 1.5T or 3T scanners with no prior harmonization. TV was determined using the DeepGRAI tool and lateral ventricular volume LVV was measured using NeuroSTREAM software.

      Results

      After propensity matching based on baseline age, disability and time of follow-up, untreated pwRRMS had significantly greater TV change when compared to treated pwRRMS (-1.2% vs. -0.3%, p = 0.044). PwRRMS treated with high-efficacy DMTs had significant and two-fold lower% LVV change when compared to pwRRMS treated on moderate-efficacy DMTs (3.5% vs. 7.0%, p = 0.001). PwRRMS who stopped DMT during the follow-up had significantly greater annualized% TV change compared to pwRRMS who remained on their DMT (-0.73% vs. -0.14%, p = 0.012) and significantly greater annualized% LVV change (3.4% vs. 1.7%, p = 0.047). These findings were also observed in a propensity analysis that additionally incorporated matching for scanner model at both baseline and follow-up visits.

      Conclusions

      LVV and TV measured on T2-FLAIR scans can detect treatment-induced short-term neurodegenerative changes measured in a real-word unstandardized, multicenter, clinical routine setting.
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