{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:37:50Z","timestamp":1791221870282,"version":"4.1.0"},"reference-count":58,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","award":["08190666"],"award-info":[{"award-number":["08190666"]}],"id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>We compared the machine learning\u2010derived, MRI\u2010based Alzheimer's disease (AD) resemblance atrophy index (AD\u2010RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p\u2010tau\n                      <jats:sub>181<\/jats:sub>\n                      , NfL), cerebrospinal fluid biomarkers (CSF) (A\u03b242, p\u2010tau\n                      <jats:sub>181<\/jats:sub>\n                      ), and apolipoprotein E (APOE) \u03b54 genotype. We defined AD using CSF A\u03b242 (A+) and p\u2010tau\n                      <jats:sub>181<\/jats:sub>\n                      (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 589 participants, 96 (16.3%) were A+T+C+. AD\u2010RAI performed better than plasma NfL when added on top of clinical features, plasma p\u2010tau\n                      <jats:sub>181<\/jats:sub>\n                      , and APOE \u03b54 genotype (area under the curve [AUC]\u00a0=\u00a00.832 vs. AUC\u00a0=\u00a00.650 among CU, AUC\u00a0=\u00a00.853 vs. AUC\u00a0=\u00a00.805 among MCI) in predicting A+T+C+.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AD\u2010RAI outperformed plasma NfL in predicting syndromal conversion of early AD.<\/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>AD\u2010RAI outperformed plasma NfL in predicting syndromal conversion among early AD.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>AD\u2010RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            Combining clinical features, plasma p\u2010tau\n                            <jats:sub>181<\/jats:sub>\n                            and apolipoprotein E (APOE) with AD\u2010RAI is the best model for predicting syndromal conversion.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.13083","type":"journal-article","created":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T09:09:01Z","timestamp":1682240941000},"page":"4987-4998","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Comparing machine learning\u2010derived MRI\u2010based and blood\u2010based neurodegeneration biomarkers in predicting syndromal conversion in early AD"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7551-2866","authenticated-orcid":false,"given":"Yuan","family":"Cai","sequence":"first","affiliation":[{"name":"Lau Tat\u2010chuen Research Centre of Brain Degenerative Diseases in Chinese Therese Pei Fong Chow Research Centre for Prevention of Dementia Lui Che Woo Institute of Innovative Medicine Gerald Choa Neuroscience Institute Li Ka Shing Institute of Health Science Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Prince of Wales Hospital  Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Lau Tat\u2010chuen Research Centre of Brain Degenerative Diseases in Chinese Therese Pei Fong Chow Research Centre for Prevention of Dementia Lui Che Woo Institute of Innovative Medicine Gerald Choa Neuroscience Institute Li Ka Shing Institute of Health Science Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Prince of Wales Hospital  Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"BrainNow Research Institute Hong Kong Science and Technology Park  Hong Kong SAR 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Diseases in Chinese Therese Pei Fong Chow Research Centre for Prevention of Dementia Lui Che Woo Institute of Innovative Medicine Gerald Choa Neuroscience Institute Li Ka Shing Institute of Health Science Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Prince of Wales Hospital  Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa Wing Chi","family":"Au","sequence":"additional","affiliation":[{"name":"Lau Tat\u2010chuen Research Centre of Brain Degenerative Diseases in Chinese Therese Pei Fong Chow Research Centre for Prevention of Dementia Lui Che Woo Institute of Innovative Medicine Gerald Choa Neuroscience Institute Li Ka Shing Institute of Health Science Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Prince of Wales Hospital  Hong Kong SAR 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