6533b7d9fe1ef96bd126cf06
RESEARCH PRODUCT
Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting
Louise-laure MarianiLouise-laure MarianiMarie VillotteAlexis BriceAlexis BriceLydia Yahia-cherifLydia Yahia-cherifLydia ChougarRomain ValabregueRomain ValabregueChristine PayanDavid GrabliEmma BiondettiEmma BiondettiRahul GauravRahul GauravOlivier ColliotOlivier ColliotJohann FaouziJohann FaouziNadya PyatigorskayaGwendoline DupontStéphane LehéricyInes PiotJean-christophe CorvolJean-christophe CorvolFlorence CormierM. VidailhetM. VidailhetBertrand DegosBertrand Degossubject
0301 basic medicinemedicine.medical_specialtyParkinson's diseaseParkinson's diseasemultiple system atrophyProgressive supranuclear palsyDiagnosis Differential03 medical and health sciences0302 clinical medicinePhysical medicine and rehabilitationParkinsonian DisordersmedicineHumansmultimodal magnetic resonance imagingReceiver operating characteristicmedicine.diagnostic_testbusiness.industryParkinsonismMagnetic resonance imagingprogressive supranuclear palsymedicine.diseaseMagnetic Resonance Imaging3. Good healthnervous system diseasesmachine learning algorithm030104 developmental biologyDiffusion Tensor ImagingNeurologyCategorizationnervous systemCohort[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Neurology (clinical)Supranuclear Palsy Progressivebusiness030217 neurology & neurosurgeryDiffusion MRIdescription
Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA-P), and 23 with MSA of the cerebellar variant (MSA-C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner-dependent effects, we tested two types of normalizations using patient data or healthy control data. Results In the replication cohort, high accuracies were achieved using volumetry in the classification of PD-PSP, PD-MSA-C, PSP-MSA-C, and PD-atypical parkinsonism (balanced accuracies: 0.840-0.983, area under the receiver operating characteristic curves: 0.907-0.995). Performances were lower for the classification of PD-MSA-P, MSA-C-MSA-P (balanced accuracies: 0.765-0.784, area under the receiver operating characteristic curve: 0.839-0.871) and PD-PSP-MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions A machine learning approach based on volumetry enabled accurate classification of subjects with early-stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-11-02 |