{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:23:39Z","timestamp":1778621019758,"version":"3.51.4"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":28,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004312","name":"Eli Lilly and Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004312","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["A&amp;D Transl Res &amp; Clin Interv"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Alzheimer's disease is partially characterized by the progressive accumulation of aggregated tau\u2010containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir\u2010positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      We quantified the disease profile of participants (\n                      <jats:italic>N<\/jats:italic>\n                      = 276) using a comprehensive set of descriptors, including clinical\/demographic (age, diagnosis, amyloid status, sex, race, ethnicity), genetic (apolipoprotein E [APOE]\u2010\u03b54), and flortaucipir\u2010PET imaging measures (regional flortaucipir standardized uptake value ratio [SUVr] and comprehensive radiomic texture features extracted from Automated Anatomical Labeling template regions). We trained an AdaBoost machine learning algorithm in a 2:1 split train\u2010test configuration to derive a prognostic index that (i)\u00a0stratifies individualized brain regions including global (AD\u2010signature region) and lobar regions (frontal, occipital, parietal, temporal) into stable\/slow\u2010 and fast\u2010progressors based on future tau accumulation, and (ii) forecasts individualized regional annualized\u2010rate\u2010of\u2010change in flortaucipir\u2010PET SUVr. Further, we developed an adaptive model incorporating flortaucipir\u2010PET measurements from the baseline and intermediate timepoints to predict annualized\u2010rate\u2010of\u2010change.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      In binary classification for predicting stable\/slow\u2010 versus fast\u2010progressors, the area\u2010under\u2010the\u2010receiver\u2010operating\u2010characteristic curve was 0.86 in the AD\u2010signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized\u2010rate\u2010of\u2010change of flortaucipir\u2010PET regional flortaucipir SUVr in AD\u2010signature and lobar regions (Pearson\u2010correlation [\n                      <jats:italic>R<\/jats:italic>\n                      ]: AD\u2010signature = 0.73; frontal = 0.73; occipital = 0.71; parietal = 0.70; temporal = 0.69). The models\u2019 performance in predicting annualized\u2010rate\u2010of\u2010change slightly increased when imaging features from intermediate timepoints were used in the adaptive setting (\n                      <jats:italic>R<\/jats:italic>\n                      : AD\u2010signature = 0.79; frontal = 0.87; occipital = 0.83; parietal = 0.74; temporal = 0.82).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Taken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Machine learning predicts the future rate of tau accumulation in Alzheimer's disease.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Tau prediction in lobar\/global regions benefits from diverse multimodal features.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>This prognostic index can serve as a sensitive tool for patient stratification.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/trc2.70005","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:19:35Z","timestamp":1730197175000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting regional tau accumulation with machine learning\u2010based tau\u2010PET and advanced radiomics"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4752-2298","authenticated-orcid":false,"given":"Saima","family":"Rathore","sequence":"first","affiliation":[{"name":"Department of Neurology and Department of Biomedical Informatics Emory University  Atlanta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ixavier A.","family":"Higgins","sequence":"additional","affiliation":[{"name":"Eli Lilly and Company  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Eli Lilly and Company  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian A.","family":"Kennedy","sequence":"additional","affiliation":[{"name":"Eli Lilly and Company  Indianapolis Indiana 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