Abstract
Background: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. Methods: We did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. Findings: In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods. Interpretations: This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials. Funding: National Institutes of Health and Parkinson's Foundation.
| Original language | English (US) |
|---|---|
| Pages (from-to) | e222-e231 |
| Journal | The Lancet Digital Health |
| Volume | 1 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 2019 |
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All Science Journal Classification (ASJC) codes
- Medicine (miscellaneous)
- Health Informatics
- Decision Sciences (miscellaneous)
- Health Information Management
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Development and validation of the automated imaging differentiation in parkinsonism (AID-P) : a multicentre machine learning study. / Archer, Derek B.; Bricker, Justin T.; Chu, Winston T.; Burciu, Roxana G.; McCracken, Johanna L.; Lai, Song; Coombes, Stephen A.; Fang, Ruogu; Barmpoutis, Angelos; Corcos, Daniel M.; Kurani, Ajay S.; Mitchell, Trina; Black, Mieniecia L.; Herschel, Ellen; Simuni, Tanya; Parrish, Todd B.; Comella, Cynthia; Xie, Tao; Seppi, Klaus; Bohnen, Nicolaas I.; Müller, Martijn LTM; Albin, Roger L.; Krismer, Florian; Du, Guangwei; Lewis, Mechelle M.; Huang, Xuemei; Li, Hong; Pasternak, Ofer; McFarland, Nikolaus R.; Okun, Michael S.; Vaillancourt, David E.
In: The Lancet Digital Health, Vol. 1, No. 5, 09.2019, p. e222-e231.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development and validation of the automated imaging differentiation in parkinsonism (AID-P)
T2 - a multicentre machine learning study
AU - Archer, Derek B.
AU - Bricker, Justin T.
AU - Chu, Winston T.
AU - Burciu, Roxana G.
AU - McCracken, Johanna L.
AU - Lai, Song
AU - Coombes, Stephen A.
AU - Fang, Ruogu
AU - Barmpoutis, Angelos
AU - Corcos, Daniel M.
AU - Kurani, Ajay S.
AU - Mitchell, Trina
AU - Black, Mieniecia L.
AU - Herschel, Ellen
AU - Simuni, Tanya
AU - Parrish, Todd B.
AU - Comella, Cynthia
AU - Xie, Tao
AU - Seppi, Klaus
AU - Bohnen, Nicolaas I.
AU - Müller, Martijn LTM
AU - Albin, Roger L.
AU - Krismer, Florian
AU - Du, Guangwei
AU - Lewis, Mechelle M.
AU - Huang, Xuemei
AU - Li, Hong
AU - Pasternak, Ofer
AU - McFarland, Nikolaus R.
AU - Okun, Michael S.
AU - Vaillancourt, David E.
PY - 2019/9
Y1 - 2019/9
N2 - Background: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. Methods: We did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. Findings: In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods. Interpretations: This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials. Funding: National Institutes of Health and Parkinson's Foundation.
AB - Background: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. Methods: We did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. Findings: In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods. Interpretations: This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials. Funding: National Institutes of Health and Parkinson's Foundation.
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U2 - 10.1016/S2589-7500(19)30105-0
DO - 10.1016/S2589-7500(19)30105-0
M3 - Article
AN - SCOPUS:85071720507
VL - 1
SP - e222-e231
JO - The Lancet Digital Health
JF - The Lancet Digital Health
SN - 2589-7500
IS - 5
ER -