Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-word study is associated with disability progression

Robert Zivadinov; Niels Bergsland; Dejan Jakimovski; Bianca Weinstock-Guttman; Ralph H B Benedict; Jon Riolo; Diego Silva; Michael G Dwyer; DeepGRAI Registry Study group

doi:10.1136/jnnp-2022-329333

Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-word study is associated with disability progression

J Neurol Neurosurg Psychiatry. 2022 Jul 28:jnnp-2022-329333. doi: 10.1136/jnnp-2022-329333. Online ahead of print.

Authors

Robert Zivadinov^{1

2}, Niels Bergsland³, Dejan Jakimovski³, Bianca Weinstock-Guttman⁴, Ralph H B Benedict⁴, Jon Riolo⁵, Diego Silva⁵, Michael G Dwyer^{3

2}; DeepGRAI Registry Study group

Collaborators

Affiliations

¹ Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA Rzivadinov@bnac.net.
² Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, Buffalo, New York, USA.
³ Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.
⁴ Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, New Jersey, USA.
⁵ Bristol Myers Squibb, New Jersey, USA.

PMID: 35902228
DOI: 10.1136/jnnp-2022-329333

Abstract

Background: The thalamus is a key grey matter structure, and sensitive marker of neurodegeneration in multiple sclerosis (MS). Previous reports indicated that thalamic volumetry using artificial intelligence (AI) on clinical-quality T2-fluid-attenuated inversion recovery (FLAIR) images alone is fast and reliable.

Objective: To investigate whether thalamic volume (TV) loss, measured longitudinally by AI, is associated with disability progression (DP) in patients with MS, participating in a large multicentre study.

Methods: The DeepGRAI (Deep Grey Rating via Artificial Intelligence) Registry is a multicentre (30 USA sites), longitudinal, observational, retrospective, real-word study of relapsing-remitting (RR) MS patients. Each centre enrolled between 30 and 35 patients. Brain MRI exams acquired at baseline and follow-up on 1.5T or 3T scanners with no prior standardisation were collected. TV measurement was performed on T2-FLAIR using DeepGRAI, and on two dimensional (D)-weighted and 3D T1-weighted images (WI) by using FMRIB's Integrated Registration and Segmentation Tool software where possible.

Results: 1002 RRMS patients were followed for an average of 2.6 years. Longitudinal TV analysis was more readily available on T2-FLAIR (96.1%), compared with 2D-T1-WI (61.8%) or 3D-T1-WI (33.2%). Over the follow-up, DeepGRAI TV loss was significantly higher in patients with DP, compared with those with disability improvement (DI) or disease stability (-1.35% in DP, -0.87% in DI and -0.57% in Stable, p=0.045, Bonferroni-adjusted, age-adjusted and follow-up time-adjusted analysis of covariance). In a regression model including MRI scanner change, age, sex, disease duration and follow-up time, DP was associated with DeepGRAI TV loss (p=0.022).

Conclusions: Thalamic atrophy measured by AI in a multicentre clinical routine real-word setting is associated with DP over mid-term follow-up.

Keywords: CLINICAL NEUROLOGY; MULTIPLE SCLEROSIS.