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Automatically computed rating scales from MRI for patients with cognitive disorders

  • Juha R. Koikkalainen
  • Hanneke F. M. Rhodius-Meester
  • Kristian S. Frederiksen
  • Marie Bruun
  • Steen G. Hasselbalch
  • Marta Baroni
  • Patrizia Mecocci
  • Ritva Vanninen
  • Anne Remes
  • Hilkka Soininen
  • Mark van Gils
  • Wiesje M. van der Flier
  • Philip Scheltens
  • Frederik Barkhof
  • Timo Erkinjuntti
  • Jyrki M. P. LötjönenEmail author
  • for the Alzheimer’s Disease Neuroimaging Initiative
Neuro

Abstract

Objectives

The aims of this study were to examine whether visual MRI rating scales used in diagnostics of cognitive disorders can be estimated computationally and to compare the visual rating scales with their computed counterparts in differential diagnostics.

Methods

A set of volumetry and voxel-based morphometry imaging biomarkers was extracted from T1-weighted and FLAIR images. A regression model was developed for estimating visual rating scale values from a combination of imaging biomarkers. We studied three visual rating scales: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and white matter hyperintensities (WMHs) measured by the Fazekas scale. Images and visual ratings from the Amsterdam Dementia Cohort (ADC) (N = 513) were used to develop the models and cross-validate them. The PredictND (N = 672) and ADNI (N = 752) cohorts were used for independent validation to test generalizability.

Results

The correlation coefficients between visual and computed rating scale values were 0.83/0.78 (MTA-left), 0.83/0.79 (MTA-right), 0.64/0.64 (GCA), and 0.76/0.75 (Fazekas) in ADC/PredictND cohorts. When performance in differential diagnostics was studied for the main types of dementia, the highest balanced accuracy, 0.75–0.86, was observed for separating different dementias from cognitively normal subjects using computed GCA. The lowest accuracy of about 0.5 for all the visual and computed scales was observed for the differentiation between Alzheimer’s disease and frontotemporal lobar degeneration. Computed scales produced higher balanced accuracies than visual scales for MTA and GCA (statistically significant).

Conclusions

MTA, GCA, and WMHs can be reliably estimated automatically helping to provide consistent imaging biomarkers for diagnosing cognitive disorders, even among less experienced readers.

Key Points

Visual rating scales used in diagnostics of cognitive disorders can be estimated computationally from MRI images with intraclass correlations ranging from 0.64 (GCA) to 0.84 (MTA).

Computed scales provided high diagnostic accuracy with single-subject data (area under the receiver operating curve range, 0.84–0.94).

Keywords

Magnetic resonance imaging Cognition disorders Atrophy 

Abbreviations

AD

Alzheimer’s disease

ADC

Amsterdam Dementia Cohort

ADNI

Alzheimer’s Disease Neuroimaging Initiative

BACC

Balanced accuracy

CN

Cognitively normal

DLB

Dementia with Lewy bodies

FLAIR

Fluid-attenuated inversion recovery

FTLD

Frontotemporal lobar degeneration

GCA

Global cortical atrophy

ICC

Intraclass correlation coefficient

MTA

Medial temporal lobe atrophy on the left (MTA-L) and right (MTA-R)

OTH

Other dementias but AD, VaD, FTLD, and DLB

VaD

Vascular dementia

VBM

Voxel-based morphometry

WMHs

White matter hyperintensities

Notes

Funding

This work has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 611005 (PredictND) and no. 601055 (VPH-DARE@IT). Research of the VUmc Alzheimer Center is part of the neurodegeneration research program of the Neuroscience Campus Amsterdam. The VUmc Alzheimer Center is supported by Stichting Alzheimer Nederland and Stichting VUmc funds. The clinical database structure of the VUmc Alzheimer Center was developed with funding from Stichting Dioraphte. FB was supported by the NIHR-UCLH Biomedical Research Centre.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Prof. Jyrki Lötjönen.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: JK and JL are employees, co-founders, and shareholders of Combinostics Ltd. JL has given an educational presentation for Merck and Sanofi that paid to his institution.

Research programs of WF have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, and Combinostics. WF has performed contract research for Boehringer Ingelheim. WF has been an invited speaker at Boehringer Ingelheim. All funding is paid to her institution.

PS has acquired grant support (for the institution) from GE Healthcare, Danone Research, Piramal, and MERCK. In the past 2 years, he has received consultancy/speaker fees (paid to the institution) from Lilly, GE Healthcare, Novartis, Probiodrug, Biogen, Roche, and EIP Pharma.

FB has consultancy payments from Biogen-Idec, TEVA, Merck-Serono, Novartis, Roche, Jansen Research, Genzyme-Sanofi, IXICO Ltd., GeNeuro, and Apitope Ltd. and payments for development of educational presentations from Biogen-IDEC and IXICO Ltd.

Statistics and biometry

One of the authors has significant statistical expertise (MvG).

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in the following:

Amsterdam Dementia Cohort (ADC): van der Flier WM, Pijnenburg YAL, Prins N, et al (2014) Optimizing patient care and research: the Amsterdam Dementia Cohort. J Alzheimers Dis. 41:313–327

Cohort from the PredictND EU FP7 project (PredictND): Bruun M, Gjerum L, Frederiksen K, et al (2017) Data-driven diagnosis of dementia disorders: the PredictND validation study. Alzheimer’s & Dementia 13(7):405–407 (Supplement)

Alzheimer’s Disease Neuroimaging Initiative (ADNI): Petersen RC, Aisen P, Beckett L, et al (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology 74(3):201–209

Methodology

• retrospective

• cross-sectional study

• multicenter study

Supplementary material

330_2019_6067_MOESM1_ESM.docx (745 kb)
ESM 1 (DOCX 744 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Juha R. Koikkalainen
    • 1
  • Hanneke F. M. Rhodius-Meester
    • 2
  • Kristian S. Frederiksen
    • 3
  • Marie Bruun
    • 3
  • Steen G. Hasselbalch
    • 3
  • Marta Baroni
    • 4
  • Patrizia Mecocci
    • 4
  • Ritva Vanninen
    • 5
    • 6
  • Anne Remes
    • 6
  • Hilkka Soininen
    • 6
  • Mark van Gils
    • 7
  • Wiesje M. van der Flier
    • 2
    • 8
  • Philip Scheltens
    • 2
  • Frederik Barkhof
    • 2
    • 9
    • 10
  • Timo Erkinjuntti
    • 11
  • Jyrki M. P. Lötjönen
    • 1
    Email author
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Combinostics Ltd.TampereFinland
  2. 2.Alzheimer Center, Department of NeurologyVU University Medical Centre, Amsterdam NeuroscienceAmsterdamthe Netherlands
  3. 3.Danish Dementia Research Centre, Department of Neurology, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  4. 4.Institute of Gerontology and GeriatricsUniversity of PerugiaPerugiaItaly
  5. 5.Institute of Clinical Medicine, RadiologyUniversity of Eastern FinlandKuopioFinland
  6. 6.Department of Clinical RadiologyKuopio University HospitalKuopioFinland
  7. 7.VTT Technical Research Center of Finland LtdTampereFinland
  8. 8.Department of Epidemiology and BiostatisticsVU University Medical CentreAmsterdamthe Netherlands
  9. 9.Institute of NeurologyUniversity College LondonLondonUK
  10. 10.Institute of Healthcare EngineeringUniversity College LondonLondonUK
  11. 11.Clinical Neurosciences, NeurologyUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland

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