Method for early prediction of neurodegenerative decline

The present invention relates to a method for predicting risks of neurodegenerative decline of a patient, especially of mild cognitive impairment (MCI). Strokes and Parkinson’s disease are frequently associated with occurrence of long-term cognitive impairment or dementia with still incompletely resolved mechanisms. The discovery of diagnostic and predictive biomarkers thus remains a major challenge. The method of the invention uses radiomics corresponding to texture features extracted from a plurality of previously-acquired medical brain images and correlated with previously-acquired clinical and/or biological data. A classifier is trained beforehand for learning these radiomics, and then operated on radiomics computed from at least one brain image of a patient to generate a score representative of its risks of neurodegenerative decline. By applying this method on a cohort of 90 MCI and non-MCI patients, the inventors show that MCI patients could be early predicted with a mean accuracy of 80%. In the same way, the method was able to discriminate very early stages of cognitive decline in a Parkinson’s disease population of 100 patients.

Keywords: IA, Predictive Model, Mild Cognitive Impairment
Patent Application number: EP18 305 244.8 on 2018/03/07
PCT/EP2019/055510 on 06/03/2019
Publications:
Transl Stroke Res 2020 Aug 11 Betrouni N et al. Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment doi: 10.1007/s12975-019-00746-3. Epub 2019 Nov 1. Mov Disord 2020 Mar 3 Betrouni N et al. Texture features of magnetic resonance images: A marker of slight cognitive deficits in Parkinson's disease doi: 10.1002/mds.27931. Epub 2019 Nov 23.

Reference:

MECA17605-D1

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Pierre MAZOT
Pierre MAZOT
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Patent filling date: 2018-03-07

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