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. Lötjönen

doi:10.1007/s00330-019-06067-1

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. Lötjönen^*

^*Corresponding author for this work

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

31 Citations (Scopus)

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).

Original language	English
Pages (from-to)	4937-4947
Journal	European Radiology
Volume	29
Issue number	9
Early online date	22 Feb 2019
DOIs	https://doi.org/10.1007/s00330-019-06067-1
Publication status	Published - 1 Sept 2019
MoE publication type	A1 Journal article-refereed

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).

Keywords

Atrophy
Cognition disorders
Magnetic resonance imaging

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Koikkalainen, J. R., Rhodius-Meester, H. F. M., Frederiksen, K. S., Bruun, M., Hasselbalch, S. G., Baroni, M., Mecocci, P., Vanninen, R., Remes, A., Soininen, H., van Gils, M., van der Flier, W. M., Scheltens, P., Barkhof, F., Erkinjuntti, T., & Lötjönen, J. M. P. (2019). Automatically computed rating scales from MRI for patients with cognitive disorders. European Radiology, 29(9), 4937-4947. https://doi.org/10.1007/s00330-019-06067-1

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title = "Automatically computed rating scales from MRI for patients with cognitive disorders",

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{\textquoteright}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 = "Atrophy, Cognition disorders, Magnetic resonance imaging",

author = "Koikkalainen, {Juha R.} and Rhodius-Meester, {Hanneke F.M.} and Frederiksen, {Kristian S.} and Marie Bruun and Hasselbalch, {Steen G.} and Marta Baroni and Patrizia Mecocci and Ritva Vanninen and Anne Remes and Hilkka Soininen and {van Gils}, Mark and {van der Flier}, {Wiesje M.} and Philip Scheltens and Frederik Barkhof and Timo Erkinjuntti and L{\"o}tj{\"o}nen, {Jyrki M.P.}",

year = "2019",

month = sep,

day = "1",

doi = "10.1007/s00330-019-06067-1",

language = "English",

volume = "29",

pages = "4937--4947",

journal = "European Radiology",

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number = "9",

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Koikkalainen, JR, Rhodius-Meester, HFM, Frederiksen, KS, Bruun, M, Hasselbalch, SG, Baroni, M, Mecocci, P, Vanninen, R, Remes, A, Soininen, H, van Gils, M, van der Flier, WM, Scheltens, P, Barkhof, F, Erkinjuntti, T & Lötjönen, JMP 2019, 'Automatically computed rating scales from MRI for patients with cognitive disorders', European Radiology, vol. 29, no. 9, pp. 4937-4947. https://doi.org/10.1007/s00330-019-06067-1

TY - JOUR

T1 - Automatically computed rating scales from MRI for patients with cognitive disorders

AU - Koikkalainen, Juha R.

AU - Rhodius-Meester, Hanneke F.M.

AU - Frederiksen, Kristian S.

AU - Bruun, Marie

AU - Hasselbalch, Steen G.

AU - Baroni, Marta

AU - Mecocci, Patrizia

AU - Vanninen, Ritva

AU - Remes, Anne

AU - Soininen, Hilkka

AU - van Gils, Mark

AU - van der Flier, Wiesje M.

AU - Scheltens, Philip

AU - Barkhof, Frederik

AU - Erkinjuntti, Timo

AU - Lötjönen, Jyrki M.P.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - 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).

AB - 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).

KW - Atrophy

KW - Cognition disorders

KW - Magnetic resonance imaging

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U2 - 10.1007/s00330-019-06067-1

DO - 10.1007/s00330-019-06067-1

M3 - Article

AN - SCOPUS:85062029418

SN - 0938-7994

VL - 29

SP - 4937

EP - 4947

JO - European Radiology

JF - European Radiology

IS - 9

ER -

Automatically computed rating scales from MRI for patients with cognitive disorders

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