Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2021;83(2):623-639.
doi: 10.3233/JAD-210450.

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study

Mandy Melissa Jane Wittens  1   2 , Diana Maria Sima  3 , Ruben Houbrechts  3 , Annemie Ribbens  3 , Ellis Niemantsverdriet  1 , Erik Fransen  4 , Christine Bastin  5 , Florence Benoit  6 , Bruno Bergmans  7 , Jean-Christophe Bier  8 , Peter Paul De Deyn  1   9 , Olivier Deryck  7 , Bernard Hanseeuw  10 , Adrian Ivanoiu  10 , Jean-Claude Lemper  11   12 , Eric Mormont  13   14 , Gaëtane Picard  15 , Ezequiel de la Rosa  3 , Eric Salmon  5   16 , Kurt Segers  17 , Anne Sieben  18 , Dirk Smeets  3 , Hanne Struyfs  1 , Evert Thiery  18 , Jos Tournoy  19 , Eric Triau  20 , Anne-Marie Vanbinst  21 , Jan Versijpt  22   2 , Maria Bjerke  1   2   23 , Sebastiaan Engelborghs  1   22   2
Affiliations
Comparative Study

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study

Mandy Melissa Jane Wittens et al. J Alzheimers Dis. 2021.

Abstract

Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer's disease (AD) dementia (ADD) patients in selected research cohorts.

Objective: This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis.

Methods: The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm's (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages.

Results: icobrain dm outperformed FreeSurfer in processing time (15-30 min versus 9-32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%).

Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.

Keywords: Alzheimer’s disease; automated volumetry; biomarkers; magnetic resonance imaging; mild cognitive impairment.

PubMed Disclaimer

Conflict of interest statement

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/21-0450r1).

Figures

Fig. 1
Fig. 1
Violin boxplots per brain region –icobrain dm. Differences between groups reported using post-hoc analysis (“Tukey” correction) for normalized brain volumes. p values: 0 ‘***’< 0.001 ‘**’< 0.01 ‘*’< 0.05. The absence of a notation corresponds to a non-significant value. p values are presented in Tables 3 and 4. HC, cognitively healthy controls; SCD, subjective cognitive decline; MCI, mild cognitive impairment; ADD, Alzheimer’s disease dementia.
Fig. 2
Fig. 2
Stepwise backward regression flowchart –icobrain dm and FreeSurfer. Final brain structures per pairwise comparison for icobrain dm (left) and FreeSurfer (right). AUC, area under the curve. Brain structures highlighted in bold are present in the final models of both automated volumetric tools. HC, cognitively healthy controls; SCD, subjective cognitive decline; MCI, mild cognitive impairment; ADD, Alzheimer’s disease dementia.
See this image and copyright information in PMC

References

    1. Yi HA, Moller C, Dieleman N, Bouwman FH, Barkhof F, Scheltens P, van der Flier WM, Vrenken H (2016) Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer’s disease. J Neurol Neurosurg Psychiatry 87, 425–432. - PubMed
    1. Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: Biomarker analysis and shared morphometry database. Sci Rep 8, 11258. - PMC - PubMed
    1. Frisoni GB, Fox NC, Jack CR Jr., Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6, 67–77. - PMC - PubMed
    1. Ridha BH, Anderson VM, Barnes J, Boyes RG, Price SL, Rossor MN, Whitwell JL, Jenkins L, Black RS, Grundman M, Fox NC (2008) Volumetric MRI and cognitive measures in Alzheimer disease: Comparison of markers of progression. J Neurol 255, 567–574. - PubMed
    1. Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB (2016) Brain atrophy in Alzheimer’s disease and aging. Ageing Res Rev 30, 25–48. - PubMed

Publication types