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Dementia"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We thoroughly investigated the ability of various machine learning models to predict clinically useful tau\u2010PET composites (load and laterality index) from low\u2010cost and non\u2010invasive features, for example, clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Models including plasma biomarkers yielded the most accurate predictions of tau\u2010PET burden (best model:\n                      <jats:italic>R<\/jats:italic>\n                      \u2010squared\u00a0=\u00a00.66\u20130.69), with especially high contribution from plasma phosphorylated tau\u2010217 (p\u2010tau217). MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model:\n                      <jats:italic>R<\/jats:italic>\n                      \u2010squared\u00a0=\u00a00.28\u20130.42). The models showed high generalizability to external test cohorts with data collected at multiple sites. Through a proof\u2010of\u2010concept two\u2010step classification workflow, we also demonstrated possible model translations to a clinical setting.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>This study highlights the promising and limiting aspects of using machine learning to predict tau\u2010PET from scalable cost\u2010effective variables, with findings relevant for clinical settings and future research.<\/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>Accessible variables showed potential in estimating tau tangle load and distribution.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Plasma phosphorylated tau\u2010217 (p\u2010tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau\u2010PET (positron emission tomography) composites.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Machine learning models demonstrated high generalizability across AD cohorts.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.14600","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T09:58:56Z","timestamp":1740218336000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Machine learning prediction of tau\u2010PET in Alzheimer's disease using plasma, MRI, and clinical data"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0630-772X","authenticated-orcid":false,"given":"Linda","family":"Karlsson","sequence":"first","affiliation":[{"name":"Clinical Memory Research Unit Department of Clinical Sciences Malm\u00f6 Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Vogel","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences SciLifeLab Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ida","family":"Arvidsson","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Sciences Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalle","family":"\u00c5str\u00f6m","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Sciences Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olof","family":"Strandberg","sequence":"additional","affiliation":[{"name":"Clinical Memory Research Unit Department of Clinical Sciences Malm\u00f6 Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob","family":"Seidlitz","sequence":"additional","affiliation":[{"name":"Penn\/CHOP Lifespan Brain Institute University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Psychiatry University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia  Philadelphia Pennsylvania USA"},{"name":"Institute for Translational Medicine and Therapeutics University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard A. 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