{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:37:40Z","timestamp":1782311860388,"version":"3.54.5"},"reference-count":55,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2024,10,8]]},"abstract":"<jats:p>Background: Early detection of amyloid-\u03b2 (A\u03b2) positivity is essential for an accurate diagnosis and treatment of Alzheimer\u2019s disease (AD), but it is currently costly and\/or invasive. Objective: We aimed to classify A\u03b2 positivity (A\u03b2+) 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 A\u03b2+, age: 73.3\u00b17.2, 150 male; 246 A\u03b2\u2013, age: 71.1\u00b17.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best A\u03b2+ 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 A\u03b2 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.<\/jats:p>","DOI":"10.3233\/jad-240366","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T10:27:12Z","timestamp":1727432832000},"page":"1293-1305","source":"Crossref","is-referenced-by-count":3,"title":["Detecting Amyloid Positivity Using Morphometric Magnetic Resonance\u00a0Imaging"],"prefix":"10.1177","volume":"101","author":[{"given":"Helena Rico","family":"Pereira","sequence":"first","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias da Universidade de Lisboa, Lisbon, Portugal"},{"name":"Faculdade de Ci\u00eancias e Tecnologia e UNINOVA-CTS, Universidade Nova de 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Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e da F\u00edsica das Radia\u00e7\u00f5es, No p\u00f3lo da Universidade Nova (LIBPhys-UNL), Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo Alexandre","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias da Universidade de Lisboa, Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-240366_ref1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1056\/NEJMoa1202753","article-title":"Clinical and biomarker changes in dominantly inherited Alzheimer\u2019s disease","volume":"367","author":"Bateman","year":"2012","journal-title":"N Engl J Med"},{"key":"10.3233\/JAD-240366_ref2","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1001\/jama.2015.4669","article-title":"Prevalence of amyloid PET positivity in dementia 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