Brain Regional Volume Estimations with NeuroQuant and FIRST: a Study in Patients with a Clinically Isolated Syndrome

Overview

Journal Neuroradiology

Specialties Neurology
Radiology

Date 2019 Mar 6

PMID 30834955

Citations 8

Authors

Deborah Pareto

Jaume Sastre-Garriga

Manel Alberich

Cristina Auger

Mar Tintore

Xavier Montalban

Alex Rovira

Affiliations

Soon will be listed here.

Abstract

Purpose: Brain volume estimates from magnetic resonance images (MRIs) are of great interest in multiple sclerosis, and several automated tools have been developed for this purpose. The goal of this study was to assess the agreement between two tools, NeuroQuant® (NQ) and FMRIB's Integrated Registration Segmentation Tool (FIRST), for estimating overall and regional brain volume in a cohort of patients with a clinically isolated syndrome (CIS). In addition, white matter lesion volume was estimated with NQ and the Lesion Segmentation Toolbox (LST).

Methods: One hundred fifteen CIS patients were analysed. Structural images were acquired on a 3.0-T system. The volume agreement between methods (by estimation of the intraclass correlation coefficient) was calculated for the right and left thalamus, caudate, putamen, pallidum, hippocampus, and amygdala, as well as for the total intracranial volume and white matter lesion volume.

Results: In general, the estimated volumes were larger by NQ than FIRST, except for the pallidum. Agreement was low (ICC < 0.40) for the smaller structures (amygdala and pallidum) and fair to good (ICC > 0.40) for the remaining ones. Agreement was fair for lesion volume (ICC = 0.61), with NQ estimates lower than LST.

Conclusions: Agreement between NQ and FIRST brain volume estimates depends on the size of the structure of interest, with larger volumes achieving better agreement. In addition, concordance between the two tools does seem to be dependent on the presence of brain lesions.

Citing Articles

Reliability of automated brain volumetric analysis: A test by comparing NeuroQuant and volBrain software.

Koussis P, Toulas P, Glotsos D, Lamprou E, Kehagias D, Lavdas E Brain Behav. 2023; 13(12):e3320.

PMID: 37997504 PMC: 10726865. DOI: 10.1002/brb3.3320.

Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging.

Warntjes J, Lundberg P, Tisell A AJNR Am J Neuroradiol. 2023; 44(8):910-915.

PMID: 37414454 PMC: 10411845. DOI: 10.3174/ajnr.A7937.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.

Mendelsohn Z, Pemberton H, Gray J, Goodkin O, Carrasco F, Scheel M Neuroradiology. 2022; 65(1):5-24.

PMID: 36331588 PMC: 9816195. DOI: 10.1007/s00234-022-03074-w.

Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant and NeuroGage in Patients With Traumatic Brain Injury.

Ross D, Seabaugh J, Seabaugh J, Barcelona J, Seabaugh D, Wright K Front Hum Neurosci. 2022; 16:715807.

PMID: 35463926 PMC: 9027332. DOI: 10.3389/fnhum.2022.715807.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.

Pemberton H, Zaki L, Goodkin O, Das R, Steketee R, Barkhof F Neuroradiology. 2021; 63(11):1773-1789.

PMID: 34476511 PMC: 8528755. DOI: 10.1007/s00234-021-02746-3.

References

Ashburner J, Friston K . Voxel-based morphometry--the methods. Neuroimage. 2000; 11(6 Pt 1):805-21. DOI: 10.1006/nimg.2000.0582. View

Smith S, Zhang Y, Jenkinson M, Chen J, Matthews P, Federico A . Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002; 17(1):479-89. DOI: 10.1006/nimg.2002.1040. View

Pelletier D, Garrison K, Henry R . Measurement of whole-brain atrophy in multiple sclerosis. J Neuroimaging. 2004; 14(3 Suppl):11S-19S. DOI: 10.1177/1051228404266264. View

Bermel R, Bakshi R . The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006; 5(2):158-70. DOI: 10.1016/S1474-4422(06)70349-0. View

Hauser S, Oksenberg J . The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration. Neuron. 2006; 52(1):61-76. DOI: 10.1016/j.neuron.2006.09.011. View

Swanton J, Rovira A, Tintore M, Altmann D, Barkhof F, Filippi M . MRI criteria for multiple sclerosis in patients presenting with clinically isolated syndromes: a multicentre retrospective study. Lancet Neurol. 2007; 6(8):677-86. DOI: 10.1016/S1474-4422(07)70176-X. View

Shrout P, Fleiss J . Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979; 86(2):420-8. DOI: 10.1037//0033-2909.86.2.420. View

Patenaude B, Smith S, Kennedy D, Jenkinson M . A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011; 56(3):907-22. PMC: 3417233. DOI: 10.1016/j.neuroimage.2011.02.046. View

Schmidt P, Gaser C, Arsic M, Buck D, Forschler A, Berthele A . An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2011; 59(4):3774-83. DOI: 10.1016/j.neuroimage.2011.11.032. View

10.

Ochs A, Ross D, Zannoni M, Abildskov T, Bigler E . Comparison of Automated Brain Volume Measures obtained with NeuroQuant and FreeSurfer. J Neuroimaging. 2015; 25(5):721-7. DOI: 10.1111/jon.12229. View

11.

Tintore M, Rovira A, Rio J, Otero-Romero S, Arrambide G, Tur C . Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain. 2015; 138(Pt 7):1863-74. DOI: 10.1093/brain/awv105. View

12.

Pareto D, Sastre-Garriga J, Aymerich F, Auger C, Tintore M, Montalban X . Lesion filling effect in regional brain volume estimations: a study in multiple sclerosis patients with low lesion load. Neuroradiology. 2016; 58(5):467-74. DOI: 10.1007/s00234-016-1654-5. View

13.

Wang C, Beadnall H, Hatton S, Bader G, Tomic D, Silva D . Automated brain volumetrics in multiple sclerosis: a step closer to clinical application. J Neurol Neurosurg Psychiatry. 2016; 87(7):754-7. PMC: 4941129. DOI: 10.1136/jnnp-2015-312304. View

14.

Gatidis S, Heber S, Storz C, Bamberg F . Population-based imaging biobanks as source of big data. Radiol Med. 2016; 122(6):430-436. DOI: 10.1007/s11547-016-0684-8. View

15.

Rocca M, Battaglini M, Benedict R, De Stefano N, Geurts J, Henry R . Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology. 2016; 88(4):403-413. PMC: 5272969. DOI: 10.1212/WNL.0000000000003542. View

16.

Sastre-Garriga J, Pareto D, Rovira A . Brain Atrophy in Multiple Sclerosis: Clinical Relevance and Technical Aspects. Neuroimaging Clin N Am. 2017; 27(2):289-300. DOI: 10.1016/j.nic.2017.01.002. View