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 |
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 › peer-review
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.
N1 - Funding Information: This study was funded by National Institutes of Health (U01 NS102038, R01 NS052318) and Parkinson's Foundation (PF-FBS-1778). Funding Information: DBA reports grants from National Institutes of Health (NIH) and Parkinson's Foundation, during the conduct of the study. JTB reports personal fees from NIH, during the conduct of the study. EH reports grants and personal fees from NIH for payment of salary, during the conduct of the study. TS reports consulting fees from Acadia, AbbVie, Amneal, Allergan, Acorda Therapeutics, Aptinyx, Denali, General Electric, Neuroderm, Neurocrine, Sanofi, Sinopia, Sunovion, TEVA, Takeda, Voyager, and US World Meds, during the conduct of the study. CC reports grants from NIH, Dystonia Medical Research Foundation, Merz Pharmaceutical, Revance Therapeutic, Retrophin, Acorda Therapeutics, and Parkinson's Foundation, during the conduct of the study, and personal fees from Acorda Therapeutics, Allergan, Lundbeck, Merz Pharmaceuticals, Acadia Pharmaceuticals, Jazz Pharmaceuticals, Neurocrine Biosciences, Revance Therapeutic, Sunovion, and AEON Biopharma, outside the submitted work. CC also received royalties from Cambridge and Wolters Kluwer. KS reports grants from FWF Austrian Science Fund, Michael J Fox Foundation, and International Parkinson and Movement Disorder Society and personal fees from Teva, UCB, Lundbeck, AOP Orphan Pharmaceuticals, AbbVie, Roche, and Grünenthal, outside the submitted work. NIB reports grants from NIH, Department of Veterans Affairs, Michael J Fox Foundation, Eisai, Axovant Sciences, and Chase Pharmaceuticals, outside the submitted work. MLTMM reports grants from NIH, Michael J Fox Foundation, and Department of Veterans Affairs, during the conduct of the study. FK reports grants from MSA Coalition, outside the submitted work. GD reports grants from National Institute of Neurological Disorders and Stroke (NINDS), NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, National Institute of Environmental Health Sciences (NIEHS), Department of Defense, and Michael J Fox Foundation, outside the submitted work. GD also has a patent pending (US 62/638,628). MML reports grants from NINDS, NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, NIEHS, Department of Defense, Michael J Fox Foundation, Biogen, and Pfizer outside the submitted work. In addition, MML also has a patent pending (US 62/638,628). XH reports grants from NINDS, NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, NIEHS, Department of Defense, Michael J Fox Foundation, Biogen, and Pfizer, outside the submitted work. XH also has a patent pending (US 62/638,628). OP reports grants from NIH and personal fees from University of Florida, during the conduct of the study. NRM reports personal fees from AbbVie, outside the submitted work. MSO serves as a consultant for the National Parkinson Foundation and has received research grants from NIH, Parkinson's Foundation, Michael J Fox Foundation, Parkinson Alliance, Smallwood Foundation, Bachmann-Strauss Foundation, Tourette Syndrome Association, and UF Foundation. MSO's deep brain stimulation research is supported by grants from NIH ( R01 NR014852 and R01NS096008 ). MSO has previously received honoraria, but in the past 60 months has received no support from industry. MSO has received royalties for publications with Demos, Manson, Amazon, Smashwords, Books4Patients, and Cambridge (movement disorders books). MSO is an associate editor for New England Journal of Medicine Journal Watch Neurology. MSO has participated in Continuing Medical Education and educational activities on movement disorders in the last 36 months sponsored by PeerView, Prime, QuantiaMD, WebMD, Medicus, MedNet, Henry Stewart, and Vanderbilt University. MSO's institution receives grants from Medtronic, AbbVie, Allergan, and ANS-St Jude, and MSO has no financial interest in these grants. MSO has participated as a site principal investigator or co-investigator for several NIH and industry sponsored trials over the years but has not received honoraria. DEV reports grants from NIH ( R01 NS075012 , R01 NS052318 , U01 NS102038 , T32 NS082169 , and R01 NS058487 ), outside the submitted work. DEV also has a patent pending for diffusion imaging in Parkinson's disease and parkinsonism ( WO2018194778A1 ) pending. All other authors declare no competing interests. Funding Information: DBA reports grants from National Institutes of Health (NIH) and Parkinson's Foundation, during the conduct of the study. JTB reports personal fees from NIH, during the conduct of the study. EH reports grants and personal fees from NIH for payment of salary, during the conduct of the study. TS reports consulting fees from Acadia, AbbVie, Amneal, Allergan, Acorda Therapeutics, Aptinyx, Denali, General Electric, Neuroderm, Neurocrine, Sanofi, Sinopia, Sunovion, TEVA, Takeda, Voyager, and US World Meds, during the conduct of the study. CC reports grants from NIH, Dystonia Medical Research Foundation, Merz Pharmaceutical, Revance Therapeutic, Retrophin, Acorda Therapeutics, and Parkinson's Foundation, during the conduct of the study, and personal fees from Acorda Therapeutics, Allergan, Lundbeck, Merz Pharmaceuticals, Acadia Pharmaceuticals, Jazz Pharmaceuticals, Neurocrine Biosciences, Revance Therapeutic, Sunovion, and AEON Biopharma, outside the submitted work. CC also received royalties from Cambridge and Wolters Kluwer. KS reports grants from FWF Austrian Science Fund, Michael J Fox Foundation, and International Parkinson and Movement Disorder Society and personal fees from Teva, UCB, Lundbeck, AOP Orphan Pharmaceuticals, AbbVie, Roche, and Gr?nenthal, outside the submitted work. NIB reports grants from NIH, Department of Veterans Affairs, Michael J Fox Foundation, Eisai, Axovant Sciences, and Chase Pharmaceuticals, outside the submitted work. MLTMM reports grants from NIH, Michael J Fox Foundation, and Department of Veterans Affairs, during the conduct of the study. FK reports grants from MSA Coalition, outside the submitted work. GD reports grants from National Institute of Neurological Disorders and Stroke (NINDS), NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, National Institute of Environmental Health Sciences (NIEHS), Department of Defense, and Michael J Fox Foundation, outside the submitted work. GD also has a patent pending (US 62/638,628). MML reports grants from NINDS, NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, NIEHS, Department of Defense, Michael J Fox Foundation, Biogen, and Pfizer outside the submitted work. In addition, MML also has a patent pending (US 62/638,628). XH reports grants from NINDS, NIH, and Michael J Fox Foundation, during the conduct of the study, and grants from NINDS, NIEHS, Department of Defense, Michael J Fox Foundation, Biogen, and Pfizer, outside the submitted work. XH also has a patent pending (US 62/638,628). OP reports grants from NIH and personal fees from University of Florida, during the conduct of the study. NRM reports personal fees from AbbVie, outside the submitted work. MSO serves as a consultant for the National Parkinson Foundation and has received research grants from NIH, Parkinson's Foundation, Michael J Fox Foundation, Parkinson Alliance, Smallwood Foundation, Bachmann-Strauss Foundation, Tourette Syndrome Association, and UF Foundation. MSO's deep brain stimulation research is supported by grants from NIH (R01 NR014852 and R01NS096008). MSO has previously received honoraria, but in the past 60 months has received no support from industry. MSO has received royalties for publications with Demos, Manson, Amazon, Smashwords, Books4Patients, and Cambridge (movement disorders books). MSO is an associate editor for New England Journal of Medicine Journal Watch Neurology. MSO has participated in Continuing Medical Education and educational activities on movement disorders in the last 36 months sponsored by PeerView, Prime, QuantiaMD, WebMD, Medicus, MedNet, Henry Stewart, and Vanderbilt University. MSO's institution receives grants from Medtronic, AbbVie, Allergan, and ANS-St Jude, and MSO has no financial interest in these grants. MSO has participated as a site principal investigator or co-investigator for several NIH and industry sponsored trials over the years but has not received honoraria. DEV reports grants from NIH (R01 NS075012, R01 NS052318, U01 NS102038, T32 NS082169, and R01 NS058487), outside the submitted work. DEV also has a patent pending for diffusion imaging in Parkinson's disease and parkinsonism (WO2018194778A1) pending. All other authors declare no competing interests. This study was funded by National Institutes of Health (U01 NS102038, R01 NS052318) and Parkinson's Foundation (PF-FBS-1778). Publisher Copyright: © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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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UR - http://www.scopus.com/inward/citedby.url?scp=85071720507&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(19)30105-0
DO - 10.1016/S2589-7500(19)30105-0
M3 - Article
C2 - 33323270
AN - SCOPUS:85071720507
VL - 1
SP - e222-e231
JO - The Lancet Digital Health
JF - The Lancet Digital Health
SN - 2589-7500
IS - 5
ER -