{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T20:54:49Z","timestamp":1773867289105,"version":"3.50.1"},"posted":{"date-parts":[[2025,10,17]]},"group-title":"Radiology and Imaging","reference-count":74,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Predicting the likelihood of developing Alzheimer\u2019s disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis but remain cognitively unimpaired. Here, we trained and evaluated ML classifiers to predict whether individuals with preclinical AD will progress to mild cognitive impairment or dementia within multiple fixed time windows, ranging from one to five years. Models were trained on regional imaging features extracted from amyloid positron emission tomography and magnetic resonance imaging pooled across seven independent sites and from two amyloid radiotracers ([\n                  <jats:sup>18<\/jats:sup>\n                  F]-florbetapir and [\n                  <jats:sup>11<\/jats:sup>\n                  C]-Pittsburgh-compound-B). Out-of-sample generalizability was evaluated via a leave-one-site-out and leave-one-tracer-out cross-validation. Classifiers achieved an out-of-sample receiver operating characteristic area-under-the-curve of 0.66 or greater when applied to all except one hold-out sites and 0.72 or greater when applied to each hold-out radiotracer. Additionally, when applying our models in a retroactive cohort enrichment analysis on A4 clinical trial data, we observed increased statistical power of detecting differences in amyloid accumulation between placebo and treatment arms after enrichment by ML stratifications. As emerging investigations of new disease-modifying therapies for AD increasingly focus on asymptomatic, preclinical populations, our findings underscore the potential applicability of ML-based patient stratification for recruiting more homogeneous cohorts and improving statistical power for detecting treatment effects for future clinical trials.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Highlights<\/jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>Machine learning can predict future cognitive impairment in preclinical Alzheimer\u2019s<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Models achieved high out-of-sample ROC-AUC on external sites and PET tracers<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Models were able to distinguish cognitively stable from decliners in the A4 cohort<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>ML cohort enrichment enhanced secondary treatment effect detection in the A4 cohort<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.1101\/2025.10.15.25337507","type":"posted-content","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:40:13Z","timestamp":1760740813000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting future cognitive impairment in preclinical Alzheimer\u2019s disease using amyloid PET and MRI: a multisite machine learning study"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2558-4132","authenticated-orcid":false,"given":"Braden","family":"Yang","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110"},{"name":"Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 19104"},{"name":"Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 19104"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8671-8424","authenticated-orcid":false,"given":"Tom","family":"Earnest","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-7422","authenticated-orcid":false,"given":"Murat","family":"Bilgel","sequence":"additional","affiliation":[{"name":"Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA 21224"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8167-8985","authenticated-orcid":false,"given":"Marilyn S.","family":"Albert","sequence":"additional","affiliation":[{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA 21205"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8501-545X","authenticated-orcid":false,"given":"Sterling C.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Wisconsin Alzheimer\u2019s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA 53726"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1025-8561","authenticated-orcid":false,"given":"Christos","family":"Davatzikos","sequence":"additional","affiliation":[{"name":"Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 19104"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6633-4861","authenticated-orcid":false,"given":"Guray","family":"Erus","sequence":"additional","affiliation":[{"name":"Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 19104"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-7940","authenticated-orcid":false,"given":"Colin L.","family":"Masters","sequence":"additional","affiliation":[{"name":"Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia 3052"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-7145","authenticated-orcid":false,"given":"Susan M.","family":"Resnick","sequence":"additional","affiliation":[{"name":"Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA 21224"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4689-3855","authenticated-orcid":false,"given":"Michael I.","family":"Miller","sequence":"additional","affiliation":[{"name":"Center of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA 21218"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0417-641X","authenticated-orcid":false,"given":"Arnold","family":"Bakker","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA 21205"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, 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