{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:23:35Z","timestamp":1777397015040,"version":"3.51.4"},"reference-count":58,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2024,4,16]]},"abstract":"<jats:p>Background: Amyloid-\u03b2 (A\u03b2) plaques play a pivotal role in Alzheimer\u2019s disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing A\u03b2 plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of A\u03b2 plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma A\u03b242\/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma A\u03b242\/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer\u2019s Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, A\u03b2+\u200arate 49.5%) and BAI (mean age 66.2, A\u03b2+\u200arate 36.9%) datasets revealed the integrated multimodal (IMM) model\u2019s superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma A\u03b242\/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer\u2019s disease diagnostics, leveraging diverse pathologic features related to A\u03b2 plaques and structural MRI.<\/jats:p>","DOI":"10.3233\/jad-231162","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T14:02:43Z","timestamp":1712066563000},"page":"1415-1426","source":"Crossref","is-referenced-by-count":6,"title":["Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques"],"prefix":"10.1177","volume":"98","author":[{"given":"Yanxi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Su","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Phoenix, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kewei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Atri","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Phoenix, AZ, USA"},{"name":"Banner Sun Health Research Institute, Sun City, AZ, USA"},{"name":"Department of Neurology, Center for Brain\/Mind Medicine, Brigham and Women\u2019s Hospital and Harvard Medical School, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard J.","family":"Caselli","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric M.","family":"Reiman","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Phoenix, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-231162_ref1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.31887\/DCNS.2003.5.1\/hhippius","article-title":"The discovery of Alzheimer\u2019s diseas","volume":"5","author":"Hippius","year":"2023","journal-title":"Dialogues Clin Neurosci"},{"key":"10.3233\/JAD-231162_ref2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.31887\/DCNS.2009.11.2\/cqiu","article-title":"Epidemiology of Alzheimer\u2019s disease: Occurrence, determinants, and strategies toward intervention","volume":"11","author":"Qiu","year":"2009","journal-title":"Dialogues Clin Neurosci"},{"key":"10.3233\/JAD-231162_ref3","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3233\/JAD-190426","article-title":"Tip of the iceberg: Assessing the global socioeconomic costs of Alzheimer\u2019s disease and related dementias and strategic implications for stakeholders","volume":"70","author":"El-Hayek","year":"2019","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-231162_ref4","first-page":"123","article-title":"Alzheimer\u2019s disease\u2013why we need early diagnosis","volume":"9","author":"Rasmussen","year":"2019","journal-title":"Degener Neurol Neuromuscul Dis"},{"key":"10.3233\/JAD-231162_ref5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1055\/s-0039-1678581","article-title":"Current and future treatments in Alzheimer\u2019s disease.","volume":"39","author":"Atri","year":"2019","journal-title":"Semin Neurol"},{"key":"10.3233\/JAD-231162_ref6","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1111\/joim.13244","article-title":"Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer\u2019s disease (MCI and dementia) in primary care: A review and synthesis","volume":"290","author":"Liss","year":"2021","journal-title":"J Intern Med"},{"key":"10.3233\/JAD-231162_ref7","first-page":"569","article-title":"Blood biomarkers from research use to clinical practice: What must be done? 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