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Article Dans Une Revue Movement Disorders Année : 2020

Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

Johann Faouzi
Rahul Gaurav
Alexis Brice
  • Fonction : Auteur
  • PersonId : 841969
David Grabli
Christine Payan
  • Fonction : Auteur

Résumé

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.
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Dates et versions

hal-03046578 , version 1 (08-12-2020)
hal-03046578 , version 2 (08-12-2020)

Identifiants

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Lydia Chougar, Johann Faouzi, Nadya Pyatigorskaya, Lydia Yahia‐cherif, Rahul Gaurav, et al.. Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting. Movement Disorders, 2020, ⟨10.1002/mds.28348⟩. ⟨hal-03046578v1⟩

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