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 |
| Number of pages | 11 |
| Journal | European Radiology |
| Volume | 29 |
| Issue number | 9 |
| Early online date | 22 Feb 2019 |
| DOIs | |
| Publication status | Published - 1 Sep 2019 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Atrophy
- Cognition disorders
- Magnetic resonance imaging
Cite this
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Automatically computed rating scales from MRI for patients with cognitive disorders. / Koikkalainen, Juha R.; Rhodius-Meester, Hanneke F.M.; Frederiksen, Kristian S.; Bruun, Marie; Hasselbalch, Steen G.; Baroni, Marta; Mecocci, Patrizia; Vanninen, Ritva; Remes, Anne; Soininen, Hilkka; van Gils, Mark; van der Flier, Wiesje M.; Scheltens, Philip; Barkhof, Frederik; Erkinjuntti, Timo; Lötjönen, Jyrki M.P. (Corresponding Author).
In: European Radiology, Vol. 29, No. 9, 01.09.2019, p. 4937-4947.Research output: Contribution to journal › Article › Scientific › peer-review
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
UR - http://www.scopus.com/inward/record.url?scp=85062029418&partnerID=8YFLogxK
U2 - 10.1007/s00330-019-06067-1
DO - 10.1007/s00330-019-06067-1
M3 - Article
AN - SCOPUS:85062029418
VL - 29
SP - 4937
EP - 4947
JO - European Radiology
JF - European Radiology
SN - 0938-7994
IS - 9
ER -