Diffusion basis spectrum imaging and diffusion tensor imaging predict persistent black hole formation in multiple sclerosis.

Diffusion basis spectrum imaging Diffusion tensor imaging Enhancing lesions Multiple sclerosis Persistent black holes

Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
11 Feb 2024
Historique:
received: 20 09 2023
revised: 13 12 2023
accepted: 10 02 2024
medline: 16 2 2024
pubmed: 16 2 2024
entrez: 15 2 2024
Statut: aheadofprint

Résumé

Diffusion basis spectrum imaging (DBSI) extracts multiple anisotropic and isotropic diffusion tensors, providing greater histopathologic specificity than diffusion tensor imaging (DTI). Persistent black holes (PBH) represent areas of severe tissue damage in multiple sclerosis (MS), and a high PBH burden is associated with worse MS disability. This study evaluated the ability of DBSI and DTI to predict which acute contrast-enhancing lesions (CELs) would persist as T In this prospective cohort study, relapsing MS patients with ≥1 CEL(s) underwent monthly MRI scans for 4 to 6 months until gadolinium resolution. DBSI and DTI metrics were quantified when the CEL was most conspicuous during the monthly scans. To determine whether the CEL became a PBH, a follow-up MRI was performed at least 12 months after the final monthly scan. The cohort included 20 MS participants (median age 33 years; 13 women) with 164 CELs. Of these, 59 (36 %) CELs evolved into PBHs. At Gd-max, DTI RD and AD of all CELs increased, and both metrics were significantly elevated for CELs which became PBHs, as compared to non-black holes (NBHs). DTI RD above 0.74 conferred an odds ratio (OR) of 7.76 (CI 3.77-15.98) for a CEL becoming a PBH (AUC 0.80, CI 0.73-0.87); DTI axial diffusivity (AD) above 1.22 conferred an OR of 7.32 (CI 3.38-15.86) for becoming a PBH (AUC 0.75, CI 0.66-0.83). DBSI RD and AD did not predict PBH development in a multivariable model. At Gd-max, DBSI restricted fraction decreased and DBSI non-restricted fraction increased in all CELs, and both metrics were significantly different for CELs which became PBHs, as compared to NBHs. A CEL with a DBSI non-restricted fraction above 0.45 had an OR of 4.77 (CI 2.35-9.66) for becoming a PBH (AUC 0.74, CI 0.66-0.81); a CEL with a DBSI restricted fraction below 0.07 had an OR of 9.58 (CI 4.59-20.02) for becoming a PBH (AUC 0.80, 0.72-0.87). Our findings suggest that greater degree of edema/extracellular space in a CEL is a predictor of tissue destruction, as evidenced by PBH evolution.

Sections du résumé

BACKGROUND AND OBJECTIVES OBJECTIVE
Diffusion basis spectrum imaging (DBSI) extracts multiple anisotropic and isotropic diffusion tensors, providing greater histopathologic specificity than diffusion tensor imaging (DTI). Persistent black holes (PBH) represent areas of severe tissue damage in multiple sclerosis (MS), and a high PBH burden is associated with worse MS disability. This study evaluated the ability of DBSI and DTI to predict which acute contrast-enhancing lesions (CELs) would persist as T
METHODS METHODS
In this prospective cohort study, relapsing MS patients with ≥1 CEL(s) underwent monthly MRI scans for 4 to 6 months until gadolinium resolution. DBSI and DTI metrics were quantified when the CEL was most conspicuous during the monthly scans. To determine whether the CEL became a PBH, a follow-up MRI was performed at least 12 months after the final monthly scan.
RESULTS RESULTS
The cohort included 20 MS participants (median age 33 years; 13 women) with 164 CELs. Of these, 59 (36 %) CELs evolved into PBHs. At Gd-max, DTI RD and AD of all CELs increased, and both metrics were significantly elevated for CELs which became PBHs, as compared to non-black holes (NBHs). DTI RD above 0.74 conferred an odds ratio (OR) of 7.76 (CI 3.77-15.98) for a CEL becoming a PBH (AUC 0.80, CI 0.73-0.87); DTI axial diffusivity (AD) above 1.22 conferred an OR of 7.32 (CI 3.38-15.86) for becoming a PBH (AUC 0.75, CI 0.66-0.83). DBSI RD and AD did not predict PBH development in a multivariable model. At Gd-max, DBSI restricted fraction decreased and DBSI non-restricted fraction increased in all CELs, and both metrics were significantly different for CELs which became PBHs, as compared to NBHs. A CEL with a DBSI non-restricted fraction above 0.45 had an OR of 4.77 (CI 2.35-9.66) for becoming a PBH (AUC 0.74, CI 0.66-0.81); a CEL with a DBSI restricted fraction below 0.07 had an OR of 9.58 (CI 4.59-20.02) for becoming a PBH (AUC 0.80, 0.72-0.87).
CONCLUSION CONCLUSIONS
Our findings suggest that greater degree of edema/extracellular space in a CEL is a predictor of tissue destruction, as evidenced by PBH evolution.

Identifiants

pubmed: 38359694
pii: S2211-0348(24)00073-7
doi: 10.1016/j.msard.2024.105494
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105494

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest L. Wooliscroft; A. Salter; G. Adusumilli; V.A. Levasseur; P. Sun; S. Lancia; D.C. Perantie; K. Trinkaus; S.K. Song; and A.H. Cross report no disclosures relevant to the manuscript. R.T. Naismith has consulted for Abata Therapeutics, Alexion Pharmaceuticals, Biogen, Bristol Myers Squibb, Celltrion, Genentech, Genzyme, Janssen, GW Therapeutics, Horizon Therapeutics, Lundbeck, NervGen, TG Therapeutics.

Auteurs

Lindsey Wooliscroft (L)

Department of Neurology, Oregon Health & Science University, Portland, OR, USA; Department of Neurology, VA Portland Health Care System, Portland, OR, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA. Electronic address: wooliscr@ohsu.edu.

Amber Salter (A)

Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Gautam Adusumilli (G)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.

Victoria A Levasseur (VA)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Minneapolis Clinic of Neurology, Coon Rapids, MN, USA.

Peng Sun (P)

Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Samantha Lancia (S)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Dana C Perantie (DC)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.

Kathryn Trinkaus (K)

Biostatistics Shared Resource, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.

Robert T Naismith (RT)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.

Sheng-Kwei Song (SK)

Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Anne H Cross (AH)

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.

Classifications MeSH