Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging.
J Alzheimers Dis
; 2024 Sep 27.
Article
en En
| MEDLINE
| ID: mdl-39331101
ABSTRACT
Background:
Early detection of amyloid-ß (Aß) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive.Objective:
We aimed to classify Aß positivity (Aß+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI).Methods:
Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aß+, age 73.3±7.2, 150 male; 246 Aß-, age 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aß+ classification model.Results:
In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions).Conclusions:
Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aß status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
J Alzheimers Dis
Asunto de la revista:
GERIATRIA
/
NEUROLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Portugal