{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:20:49Z","timestamp":1788254449884,"version":"build-2803163510"},"reference-count":41,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate, non-invasive prediction of cerebral amyloid-\u03b2 (A\u03b2) pathology in mild cognitive impairment (MCI) remains challenging yet critical for early intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a multimodal machine learning model integrating clinical features, plasma biomarkers, and structural MRI metrics for non-invasive A\u03b2 prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were obtained from the Alzheimer's Disease Neuroimaging Initiative. Participants with concurrent plasma biomarkers, 3D T1-weighted MRI, and amyloid assessments were included. Logistic Regression, Decision Tree, and Support Vector Machine models were constructed using clinical, plasma, MRI, and combined features. Performance was evaluated via internal validation and external testing in a cognitively unimpaired cohort using AUC, calibration curves, and decision curve analysis. The prognostic value of the model-derived A\u03b2 risk probability was assessed using Cox regression in an independent longitudinal MCI cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The optimal Logistic Regression model incorporated\n                      <jats:italic toggle=\"yes\">APOE<\/jats:italic>\n                      \u03b54 status, Mini-Mental State Examination score, plasma p-Tau217, A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>40<\/jats:sub>\n                      ratio, and bilateral hippocampal and left amygdalar volumes. The combined model achieved an AUC of 0.875 in internal validation and maintained robust performance in the external unimpaired cohort (AUC\u2009=\u20090.883), outperforming single-modality models. The predicted A\u03b2-positive risk probability effectively stratified disease progression risk in MCI patients (C-index\u2009=\u20090.771).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A multimodal model integrating plasma and MRI features accurately predicts A\u03b2 pathology and progression risk, offering a practical non-invasive tool for early Alzheimer's disease screening and risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261465672","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:40:15Z","timestamp":1784770815000},"page":"101-114","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal machine learning model integrating plasma biomarkers and MRI metrics for non-invasive prediction of amyloid-\u03b2 pathology in mild cognitive impairment"],"prefix":"10.1177","volume":"113","author":[{"given":"Qiaofang","family":"Fang","sequence":"first","affiliation":[{"name":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University"},{"name":"Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyu","family":"Guo","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University"},{"name":"Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linkai","family":"Chen","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University"},{"name":"Key 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