Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study

Derek B Archer; Justin T Bricker; Winston T Chu; Roxana G Burciu; Johanna L McCracken; Song Lai; Stephen A Coombes; Ruogu Fang; Angelos Barmpoutis; Daniel M Corcos; Ajay S Kurani; Trina Mitchell; Mieniecia L Black; Ellen Herschel; Tanya Simuni; Todd B Parrish; Cynthia Comella; Tao Xie; Klaus Seppi; Nicolaas I Bohnen; Martijn Ltm Müller; Roger L Albin; Florian Krismer; Guangwei Du; Mechelle M Lewis; Xuemei Huang; Hong Li; Ofer Pasternak; Nikolaus R McFarland; Michael S Okun; David E Vaillancourt

doi:10.1016/S2589-7500(19)30105-0

Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study

Lancet Digit Health. 2019 Sep;1(5):e222-e231. doi: 10.1016/S2589-7500(19)30105-0. Epub 2019 Aug 27.

Authors

Derek B Archer¹, Justin T Bricker¹, Winston T Chu², Roxana G Burciu³, Johanna L McCracken¹, Song Lai⁴, Stephen A Coombes¹, Ruogu Fang⁵, Angelos Barmpoutis⁶, Daniel M Corcos⁷, Ajay S Kurani⁸, Trina Mitchell¹, Mieniecia L Black¹, Ellen Herschel⁹, Tanya Simuni⁹, Todd B Parrish⁸, Cynthia Comella⁷, Tao Xie¹⁰, Klaus Seppi¹¹, Nicolaas I Bohnen¹², Martijn Ltm Müller¹³, Roger L Albin¹⁴, Florian Krismer¹¹, Guangwei Du¹⁵, Mechelle M Lewis¹⁶, Xuemei Huang¹⁷, Hong Li¹⁸, Ofer Pasternak¹⁹, Nikolaus R McFarland²⁰, Michael S Okun²¹, David E Vaillancourt²²

Affiliations

¹ Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.
² Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA; J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
³ Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, USA.
⁴ Department of Radiation Oncology, University of Florida, Gainesville, FL, USA; CTSI Human Imaging Core, University of Florida, Gainesville, FL, USA.
⁵ J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
⁶ J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Digital Worlds Institute, University of Florida, Gainesville, FL, USA.
⁷ Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
⁸ Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁹ Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
¹⁰ Department of Neurology, University of Chicago, Chicago, IL, USA.
¹¹ Neuroimaging Research Core Facility, Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
¹² Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Neurology Service and Geriatrics Research, Education, and Clinical Center, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA.
¹³ Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA.
¹⁴ Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Neurology Service and Geriatrics Research, Education, and Clinical Center, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA.
¹⁵ Department of Neurology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA.
¹⁶ Department of Neurology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA; Department of Pharmacology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA.
¹⁷ Department of Neurology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA; Department of Pharmacology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA; Department of Neurosurgery, Penn State Milton S Hershey Medical Center, Hershey, PA, USA; Department of Radiology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA; Department of Kinesiology, Penn State Milton S Hershey Medical Center, Hershey, PA, USA.
¹⁸ Department of Public Health Sciences, Medical College of South Carolina, Charleston, SC, USA.
¹⁹ Department of Psychiatry and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁰ Fixel Institute for Neurological Disease, University of Florida, Gainesville, FL, USA; Department of Neurology, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
²¹ Fixel Institute for Neurological Disease, University of Florida, Gainesville, FL, USA; Department of Neurology, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
²² Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA; J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Fixel Institute for Neurological Disease, University of Florida, Gainesville, FL, USA; Department of Neurology, McKnight Brain Institute, University of Florida, Gainesville, FL, USA. Electronic address: vcourt@ufl.edu.

PMID: 33323270
DOI: 10.1016/S2589-7500(19)30105-0

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.

Publication types

Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Aged
Anisotropy
Austria
Biomarkers*
Brain
Cohort Studies
Diffusion Magnetic Resonance Imaging / statistics & numerical data
Female
Germany
Humans
Machine Learning*
Male
Middle Aged
Multiple System Atrophy / diagnosis*
Parkinson Disease / diagnosis
Parkinsonian Disorders / diagnosis*
Reproducibility of Results
Supranuclear Palsy, Progressive / diagnosis*
United States

Substances

Biomarkers

Abstract

Publication types

MeSH terms

Substances

Grants and funding