Differential diagnosis of neurodegenerative diseases using structural MRI data
Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differen...
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| Published in | NeuroImage clinical Vol. 11; no. C; pp. 435 - 449 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2016
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia.
Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making.
A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods.
The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.
[Display omitted]
•Differential diagnostics of dementias was studied using structural MRI data.•504 patients with both T1 and FLAIR MRIs from five patient classes were evaluated.•Different fully automatic quantification methods were compared and combined.•Classification accuracy of 70.6% was obtained for 5-class classification problem.•Combination of several quantification methods was needed for optimal accuracy. |
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| AbstractList | AbstractDifferent neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. [Display omitted] •Differential diagnostics of dementias was studied using structural MRI data.•504 patients with both T1 and FLAIR MRIs from five patient classes were evaluated.•Different fully automatic quantification methods were compared and combined.•Classification accuracy of 70.6% was obtained for 5-class classification problem.•Combination of several quantification methods was needed for optimal accuracy. Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. Image 1 • Differential diagnostics of dementias was studied using structural MRI data. • 504 patients with both T1 and FLAIR MRIs from five patient classes were evaluated. • Different fully automatic quantification methods were compared and combined. • Classification accuracy of 70.6% was obtained for 5-class classification problem. • Combination of several quantification methods was needed for optimal accuracy. Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. |
| Author | Guerrero, Ricardo Remes, Anne M. Waldemar, Gunhild Hasselbalch, Steen Ledig, Christian Tijms, Betty Lötjönen, Jyrki Koikkalainen, Juha Tong, Tong Mecocci, Patrizia Schuh, Andreas Rhodius-Meester, Hanneke Tolonen, Antti van der Flier, Wiesje Barkhof, Frederik Rueckert, Daniel Soininen, Hilkka Lemstra, Afina W. |
| AuthorAffiliation | e Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland f Department of Epidemiology and Biostatistics, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands d Department of Computing, Imperial College London, London, UK h Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy b Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands i Combinostics Ltd., Tampere, Finland a VTT Technical Research Centre of Finland, Tampere, Finland c Department of Radiology and Nuclear Medicine, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands g Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark |
| AuthorAffiliation_xml | – name: h Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy – name: f Department of Epidemiology and Biostatistics, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – name: c Department of Radiology and Nuclear Medicine, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – name: i Combinostics Ltd., Tampere, Finland – name: b Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – name: a VTT Technical Research Centre of Finland, Tampere, Finland – name: d Department of Computing, Imperial College London, London, UK – name: e Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland – name: g Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark |
| Author_xml | – sequence: 1 givenname: Juha surname: Koikkalainen fullname: Koikkalainen, Juha email: juha.koikkalainen@combinostics.com organization: VTT Technical Research Centre of Finland, Tampere, Finland – sequence: 2 givenname: Hanneke surname: Rhodius-Meester fullname: Rhodius-Meester, Hanneke organization: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – sequence: 3 givenname: Antti surname: Tolonen fullname: Tolonen, Antti organization: VTT Technical Research Centre of Finland, Tampere, Finland – sequence: 4 givenname: Frederik surname: Barkhof fullname: Barkhof, Frederik organization: Department of Radiology and Nuclear Medicine, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – sequence: 5 givenname: Betty surname: Tijms fullname: Tijms, Betty organization: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – sequence: 6 givenname: Afina W. surname: Lemstra fullname: Lemstra, Afina W. organization: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – sequence: 7 givenname: Tong surname: Tong fullname: Tong, Tong organization: Department of Computing, Imperial College London, London, UK – sequence: 8 givenname: Ricardo surname: Guerrero fullname: Guerrero, Ricardo organization: Department of Computing, Imperial College London, London, UK – sequence: 9 givenname: Andreas surname: Schuh fullname: Schuh, Andreas organization: Department of Computing, Imperial College London, London, UK – sequence: 10 givenname: Christian surname: Ledig fullname: Ledig, Christian organization: Department of Computing, Imperial College London, London, UK – sequence: 11 givenname: Daniel surname: Rueckert fullname: Rueckert, Daniel organization: Department of Computing, Imperial College London, London, UK – sequence: 12 givenname: Hilkka surname: Soininen fullname: Soininen, Hilkka organization: Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland – sequence: 13 givenname: Anne M. surname: Remes fullname: Remes, Anne M. organization: Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland – sequence: 14 givenname: Gunhild surname: Waldemar fullname: Waldemar, Gunhild organization: Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark – sequence: 15 givenname: Steen surname: Hasselbalch fullname: Hasselbalch, Steen organization: Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark – sequence: 16 givenname: Patrizia surname: Mecocci fullname: Mecocci, Patrizia organization: Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy – sequence: 17 givenname: Wiesje surname: van der Flier fullname: van der Flier, Wiesje organization: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands – sequence: 18 givenname: Jyrki surname: Lötjönen fullname: Lötjönen, Jyrki organization: VTT Technical Research Centre of Finland, Tampere, Finland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27104138$$D View this record in MEDLINE/PubMed |
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| Copyright | 2016 Commonwealth Scientific and Industrial Research Organisation Commonwealth Scientific and Industrial Research Organisation 2016 The Authors 2016 |
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