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.