4.5 Article

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

Journal

JOURNAL OF ALZHEIMERS DISEASE
Volume 74, Issue 4, Pages 1157-1166

Publisher

IOS PRESS
DOI: 10.3233/JAD-190594

Keywords

All cognitive disorders/dementia; Alzheimer's disease; assessment of cognitive disorders/dementia; magnetic resonance imaging; volumetric MRI

Categories

Funding

  1. Contrat d'Interface Local from Assistance Publique-Hopitaux de Paris (APHP)
  2. AXA Research Fund
  3. Fondation Universit e Pierre et Marie Curie
  4. Fondation pour la Recherche sur Alzheimer, Paris, France
  5. European Union [666992]
  6. French government under Agence Nationale de la Recherche [ANR-19-P3IA-0001, ANR-10-IAIHU-06, ANR-11-IDEX-004, SU-16-R-EMR-16]
  7. ICM Big Brain Theory Program (project DYNAMO)
  8. Abeona Foundation (project Brain@Scale)
  9. Contrat d'Interface Local program from Assistance Publique-Hopitaux de Paris (AP-HP)
  10. Agence Nationale de la Recherche [ANR-14-CE15-0016-07]
  11. joint NSF/NIH/ANR program Collaborative Research in Computational Neuroscience (project HIPLAY7) [ANR-16-NEUC-0001-01]
  12. Agence Nationale de la Recherche (ANR) [ANR-14-CE15-0016, ANR-16-NEUC-0001] Funding Source: Agence Nationale de la Recherche (ANR)

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Background: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets. Objective: Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic. Methods: We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader (TM)); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria. Results: Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM. Conclusion: In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.

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