{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:21:40Z","timestamp":1787235700257,"version":"build-2736575974"},"reference-count":40,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"PDRadiopharma Inc."}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Amyloid-\u03b2 (A\u03b2) deposit prediction accuracy is necessary for clinicians treating patients desiring Alzheimer's disease (AD) modifying drugs. A\u03b2-PET imaging is useful for diagnosis, but high in cost compared to brain perfusion SPECT. However, SPECT displays regional cerebral blood flow (rCBF) and does not detect A\u03b2 deposits, therefore requiring additional clinical information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      This article describes a novel statistical method to predict amyloid deposits via PET images (A\u03b2+ or A\u03b2\u2212) using the three indices of the\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-ECD SPECT \u2013 severity, extend, and ratio \u2013 for the three ROIs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Candidate patients (N\u2009=\u2009114 patients [55% male], 81 A\u03b2+ 33 A\u03b2\u2212, mean age 74.2\u2009\u00b1\u20096.6 years, mean MMSE score 23.7\u2009\u00b1\u20092.8) underwent MRI and\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-ECD SPECT scanning. After examining SPECT index, demographic, and age data, age and sex were treated as confounders in one, two, and three-index logistic additive models with severity, extend, and ratio as explanatory variables. Area under curve (AUC), sensitivity and specificity were used as statistical indices for model fitness and accuracies. Three-hold cross validation analyses were conducted to evaluate error rates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>According to ROC analysis, best scores for fitness and accuracy were obtained from the three-index model with patients\u2019 age and sex for the configured ROIs including precuneus, posterior cingulate and temporal-parietal region of SPECT (AUC: 0.818, Sensitivity: 0.803, Specificity: 0.727).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This technique using\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-ECD SPECT data can predict amyloid deposits with acceptable accuracy. To confirm the reliability and validity, a multicenter SPECT study is needed.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251324222","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T16:43:07Z","timestamp":1742575387000},"page":"1281-1289","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A statistical method for predicting amyloid-\u03b2 deposits from severity, extend, and ratio indices of the\n                    <sup>99m<\/sup>\n                    Tc-ECD SPECT"],"prefix":"10.1177","volume":"104","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4621-9366","authenticated-orcid":false,"given":"Takashi","family":"Asada","sequence":"first","affiliation":[{"name":"Memory Clinic Ochanomizu, Bunkyo-ku, Tokyo, Japan"},{"name":"Faculty of Medicine, Institute of Science Tokyo, Bunkyo-ku, Tokyo, 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