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

Mandy Melissa Jane Wittens; Diana Maria Sima; Ruben Houbrechts; Annemie Ribbens; Ellis Niemantsverdriet; Erik Fransen; Christine Bastin; Florence Benoit; Bruno Bergmans; Jean-Christophe Bier; Peter Paul De Deyn; Olivier Deryck; Bernard Hanseeuw; Adrian Ivanoiu; Jean-Claude Lemper; Eric Mormont; Gaëtane Picard; Ezequiel de la Rosa; Eric Salmon; Kurt Segers; Anne Sieben; Dirk Smeets; Hanne Struyfs; Evert Thiery; Jos Tournoy; Eric Triau; Anne-Marie Vanbinst; Jan Versijpt; Maria Bjerke; Sebastiaan Engelborghs

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, Diana Maria Sima, Ruben Houbrechts, Annemie Ribbens, Ellis Niemantsverdriet, Erik Fransen, Christine Bastin, Florence Benoit, Bruno Bergmans, Jean-Christophe Bier, Peter Paul De Deyn, Olivier Deryck, Bernard Hanseeuw, Adrian Ivanoiu, Jean-Claude Lemper, Eric Mormont, Gaëtane Picard, Ezequiel de la Rosa, Eric Salmon, Kurt SegersAnne Sieben, Dirk Smeets, Hanne Struyfs, Evert Thiery, Jos Tournoy, Eric Triau, Anne-Marie Vanbinst, Jan Versijpt, Maria Bjerke, Sebastiaan Engelborghs

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

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.

Original language	English
Pages (from-to)	623-639
Number of pages	17
Journal	Journal of Alzheimer's Disease
Volume	83
Issue number	2
DOIs	https://doi.org/10.3233/JAD-210450
Publication status	Published - 2021

Bibliographical note

Funding Information:
This research was in part supported by the agency of Flanders Innovation & Intrepreneurship (VLAIO), the Flemish Agency for Innovation by Science and Technology (IWT 140262), the Interreg V programme Flanders-The Netherlands of the European Regional Development Fund (ERDF) (Herinner-ingen/Memories project), the European Union’s Horizon 2020 research and innovation programme under grant agreement numbers 666992 (EURO-POND) and 765148 (TRABIT). For the University of Liège center, this work was supported by a French Speaking Community Concerted Research Action (ARC-06/11-340) and a Belgian InterUniversity Attraction Pole (P6/29). We acknowledge the contribution of Sebastiaan Mariën and Roxanne Bladt.

Funding Information:
The study was approved by the ethics committee of the University of Antwerp / Universitair Ziekenhuis Antwerp (N-16/2/18), Antwerp and by the ethics committees of Algemeen Ziekenhuis Sint-Jan Brugge-Oostende, Bruges (N-1992); Centre Hospitalier Universitaire Brugmann (CHU Brugmann), Brussels (N-2016/84); Centre Hospitalier Universitaire Liege (CHU Liege), Liege (N-2012/274); Cliniques Universitaires de Bruxelles (ULB), Hopital Erasme, Brussels (N-P2016/187); Cliniques Universitaires Saint-Luc (UCL), Brussels (N-2016/07jui/261); Clinique St-Pierre Ottignies, Ottignies (N-OM045); Universitair Ziekenhuis Brussel, Brussels (N-2016/183); and Ziekenhuis Netwerk Antwerp (ZNA), Antwerp (N-4730).

Publisher Copyright:
© 2021 - The authors.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

Keywords

Alzheimer’s disease
automated volumetry
biomarkers
magnetic resonance imaging
mild cognitive impairment

Access to Document

10.3233/JAD-210450

Cite this

Wittens, M. M. J., Sima, D. M., Houbrechts, R., Ribbens, A., Niemantsverdriet, E., Fransen, E., Bastin, C., Benoit, F., Bergmans, B., Bier, J-C., De Deyn, P. P., Deryck, O., Hanseeuw, B., Ivanoiu, A., Lemper, J-C., Mormont, E., Picard, G., de la Rosa, E., Salmon, E., ... Engelborghs, S. (2021). Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study. Journal of Alzheimer's Disease, 83(2), 623-639. https://doi.org/10.3233/JAD-210450

Wittens, Mandy Melissa Jane ; Sima, Diana Maria ; Houbrechts, Ruben ; Ribbens, Annemie ; Niemantsverdriet, Ellis ; Fransen, Erik ; Bastin, Christine ; Benoit, Florence ; Bergmans, Bruno ; Bier, Jean-Christophe ; De Deyn, Peter Paul ; Deryck, Olivier ; Hanseeuw, Bernard ; Ivanoiu, Adrian ; Lemper, Jean-Claude ; Mormont, Eric ; Picard, Gaëtane ; de la Rosa, Ezequiel ; Salmon, Eric ; Segers, Kurt ; Sieben, Anne ; Smeets, Dirk ; Struyfs, Hanne ; Thiery, Evert ; Tournoy, Jos ; Triau, Eric ; Vanbinst, Anne-Marie ; Versijpt, Jan ; Bjerke, Maria ; Engelborghs, Sebastiaan. / Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting : A REMEMBER Study. In: Journal of Alzheimer's Disease. 2021 ; Vol. 83, No. 2. pp. 623-639.

@article{0ea2bb9e845c4875ac2ff1abf624ba6d,

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

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{\textquoteright}s disease, automated volumetry, biomarkers, magnetic resonance imaging, mild cognitive impairment",

author = "Wittens, {Mandy Melissa Jane} and Sima, {Diana Maria} and Ruben Houbrechts and Annemie Ribbens and Ellis Niemantsverdriet and Erik Fransen and Christine Bastin and Florence Benoit and Bruno Bergmans and Jean-Christophe Bier and {De Deyn}, {Peter Paul} and Olivier Deryck and Bernard Hanseeuw and Adrian Ivanoiu and Jean-Claude Lemper and Eric Mormont and Ga{\"e}tane Picard and {de la Rosa}, Ezequiel and Eric Salmon and Kurt Segers and Anne Sieben and Dirk Smeets and Hanne Struyfs and Evert Thiery and Jos Tournoy and Eric Triau and Anne-Marie Vanbinst and Jan Versijpt and Maria Bjerke and Sebastiaan Engelborghs",

note = "Funding Information: This research was in part supported by the agency of Flanders Innovation & Intrepreneurship (VLAIO), the Flemish Agency for Innovation by Science and Technology (IWT 140262), the Interreg V programme Flanders-The Netherlands of the European Regional Development Fund (ERDF) (Herinner-ingen/Memories project), the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement numbers 666992 (EURO-POND) and 765148 (TRABIT). For the University of Li{\`e}ge center, this work was supported by a French Speaking Community Concerted Research Action (ARC-06/11-340) and a Belgian InterUniversity Attraction Pole (P6/29). We acknowledge the contribution of Sebastiaan Mari{\"e}n and Roxanne Bladt. Funding Information: The study was approved by the ethics committee of the University of Antwerp / Universitair Ziekenhuis Antwerp (N-16/2/18), Antwerp and by the ethics committees of Algemeen Ziekenhuis Sint-Jan Brugge-Oostende, Bruges (N-1992); Centre Hospitalier Universitaire Brugmann (CHU Brugmann), Brussels (N-2016/84); Centre Hospitalier Universitaire Liege (CHU Liege), Liege (N-2012/274); Cliniques Universitaires de Bruxelles (ULB), Hopital Erasme, Brussels (N-P2016/187); Cliniques Universitaires Saint-Luc (UCL), Brussels (N-2016/07jui/261); Clinique St-Pierre Ottignies, Ottignies (N-OM045); Universitair Ziekenhuis Brussel, Brussels (N-2016/183); and Ziekenhuis Netwerk Antwerp (ZNA), Antwerp (N-4730). Publisher Copyright: {\textcopyright} 2021 - The authors. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.",

year = "2021",

doi = "10.3233/JAD-210450",

language = "English",

volume = "83",

pages = "623--639",

journal = "Journal of Alzheimer's Disease",

issn = "1387-2877",

publisher = "IOS Press",

number = "2",

}

Wittens, MMJ, Sima, DM, Houbrechts, R, Ribbens, A, Niemantsverdriet, E, Fransen, E, Bastin, C, Benoit, F, Bergmans, B, Bier, J-C, De Deyn, PP, Deryck, O, Hanseeuw, B, Ivanoiu, A, Lemper, J-C, Mormont, E, Picard, G, de la Rosa, E, Salmon, E, Segers, K, Sieben, A, Smeets, D, Struyfs, H, Thiery, E, Tournoy, J, Triau, E, Vanbinst, A-M , Versijpt, J , Bjerke, M & Engelborghs, S 2021, 'Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study', Journal of Alzheimer's Disease, vol. 83, no. 2, pp. 623-639. https://doi.org/10.3233/JAD-210450

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting : A REMEMBER Study. / Wittens, Mandy Melissa Jane; Sima, Diana Maria; Houbrechts, Ruben; Ribbens, Annemie; Niemantsverdriet, Ellis; Fransen, Erik; Bastin, Christine; Benoit, Florence; Bergmans, Bruno; Bier, Jean-Christophe; De Deyn, Peter Paul; Deryck, Olivier; Hanseeuw, Bernard; Ivanoiu, Adrian; Lemper, Jean-Claude; Mormont, Eric; Picard, Gaëtane; de la Rosa, Ezequiel; Salmon, Eric; Segers, Kurt; Sieben, Anne; Smeets, Dirk; Struyfs, Hanne; Thiery, Evert; Tournoy, Jos; Triau, Eric; Vanbinst, Anne-Marie ; Versijpt, Jan ; Bjerke, Maria ; Engelborghs, Sebastiaan.

In: Journal of Alzheimer's Disease, Vol. 83, No. 2, 2021, p. 623-639.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting

T2 - A REMEMBER Study

AU - Wittens, Mandy Melissa Jane

AU - Sima, Diana Maria

AU - Houbrechts, Ruben

AU - Ribbens, Annemie

AU - Niemantsverdriet, Ellis

AU - Fransen, Erik

AU - Bastin, Christine

AU - Benoit, Florence

AU - Bergmans, Bruno

AU - Bier, Jean-Christophe

AU - De Deyn, Peter Paul

AU - Deryck, Olivier

AU - Hanseeuw, Bernard

AU - Ivanoiu, Adrian

AU - Lemper, Jean-Claude

AU - Mormont, Eric

AU - Picard, Gaëtane

AU - de la Rosa, Ezequiel

AU - Salmon, Eric

AU - Segers, Kurt

AU - Sieben, Anne

AU - Smeets, Dirk

AU - Struyfs, Hanne

AU - Thiery, Evert

AU - Tournoy, Jos

AU - Triau, Eric

AU - Vanbinst, Anne-Marie

AU - Versijpt, Jan

AU - Bjerke, Maria

AU - Engelborghs, Sebastiaan

N1 - Funding Information: This research was in part supported by the agency of Flanders Innovation & Intrepreneurship (VLAIO), the Flemish Agency for Innovation by Science and Technology (IWT 140262), the Interreg V programme Flanders-The Netherlands of the European Regional Development Fund (ERDF) (Herinner-ingen/Memories project), the European Union’s Horizon 2020 research and innovation programme under grant agreement numbers 666992 (EURO-POND) and 765148 (TRABIT). For the University of Liège center, this work was supported by a French Speaking Community Concerted Research Action (ARC-06/11-340) and a Belgian InterUniversity Attraction Pole (P6/29). We acknowledge the contribution of Sebastiaan Mariën and Roxanne Bladt. Funding Information: The study was approved by the ethics committee of the University of Antwerp / Universitair Ziekenhuis Antwerp (N-16/2/18), Antwerp and by the ethics committees of Algemeen Ziekenhuis Sint-Jan Brugge-Oostende, Bruges (N-1992); Centre Hospitalier Universitaire Brugmann (CHU Brugmann), Brussels (N-2016/84); Centre Hospitalier Universitaire Liege (CHU Liege), Liege (N-2012/274); Cliniques Universitaires de Bruxelles (ULB), Hopital Erasme, Brussels (N-P2016/187); Cliniques Universitaires Saint-Luc (UCL), Brussels (N-2016/07jui/261); Clinique St-Pierre Ottignies, Ottignies (N-OM045); Universitair Ziekenhuis Brussel, Brussels (N-2016/183); and Ziekenhuis Netwerk Antwerp (ZNA), Antwerp (N-4730). Publisher Copyright: © 2021 - The authors. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Alzheimer’s disease

KW - automated volumetry

KW - biomarkers

KW - magnetic resonance imaging

KW - mild cognitive impairment

UR - http://www.scopus.com/inward/record.url?scp=85115276030&partnerID=8YFLogxK

U2 - 10.3233/JAD-210450

DO - 10.3233/JAD-210450

M3 - Article

C2 - 34334402

VL - 83

SP - 623

EP - 639

JO - Journal of Alzheimer's Disease

JF - Journal of Alzheimer's Disease

SN - 1387-2877

IS - 2

ER -

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

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this