Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach.
Giuseppe PontilloSimone PennaSirio CocozzaMario QuarantelliMichela GravinaRoberta LanzilloStefano MarroneTeresa CostabileMatilde IngleseVincenzo Brescia MorraDaniele RiccioAndrea ElefanteMaria PetraccaCarlo SansoneArturo BrunettiPublished in: European radiology (2022)
• The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course.
Keyphrases
- multiple sclerosis
- machine learning
- end stage renal disease
- white matter
- newly diagnosed
- ejection fraction
- magnetic resonance imaging
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- resting state
- oxidative stress
- patient reported outcomes
- magnetic resonance
- contrast enhanced
- brain injury
- cerebral ischemia
- drug induced