Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting
| Autor: | Emma Biondetti, Louise-Laure Mariani, Johann Faouzi, Bertrand Degos, Alexis Brice, M. Vidailhet, Jean-Christophe Corvol, Lydia Chougar, Marie Villotte, Olivier Colliot, Gwendoline Dupont, Nadya Pyatigorskaya, Romain Valabregue, David Grabli, Florence Cormier, Stéphane Lehéricy, Christine Payan, Ines Piot, Rahul Gaurav |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry Parkinsonism Magnetic resonance imaging medicine.disease Training cohort nervous system diseases 030218 nuclear medicine & medical imaging Parkinsonian syndromes 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation stomatognathic system nervous system Categorization mental disorders Replication (statistics) Research environment Cohort medicine business 030217 neurology & neurosurgery |
| DOI: | 10.1101/2020.03.27.20042671 |
| Popis: | BackgroundSeveral studies have shown that machine learning algorithms using MRI data can accurately discriminate parkinsonian syndromes. Validation under clinical conditions is missing.ObjectivesTo evaluate the accuracy for the categorization of parkinsonian syndromes of a machine learning algorithm trained with a research cohort and tested on an independent clinical replication cohort.Methods361 subjects, including 94 healthy controls, 139 patients with PD, 60 with PSP with Richardson’s syndrome, 41 with MSA of the parkinsonian variant (MSA-P) and 27 with MSA of the cerebellar variant (MSA-P), were recruited. They were divided into a training cohort (n=179) scanned in a research environment, and a replication cohort (n=182), scanned in clinical conditions on different MRI systems. Volumes and DTI metrics in 13 brain regions were used as input for a supervised machine learning algorithm.ResultHigh accuracy was achieved using volumetry in the classification of PD versus PSP, PD versus MSA-P, PD versus MSA-C, PD versus atypical parkinsonian syndromes and PSP versus MSA-C in both cohorts, although slightly lower in the replication cohort (balanced accuracy: 0.800 to 0.915 in the training cohort; 0.741 to 0.928 in the replication cohort). Performance was lower in the classification of PSP versus MSA-P and MSA-P versus MSA-C. When adding DTI metrics, the performance tended to increase in the training cohort, but not in the replication cohort.ConclusionsA machine learning approach based on volumetric and DTI data can accurately classify subjects with early-stage parkinsonism, scanned on different MRI systems, in the setting of their clinical workup. |
| Databáze: | OpenAIRE |
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