{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T08:43:01Z","timestamp":1782549781234,"version":"3.54.5"},"reference-count":68,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda eInnovaci\u00f3n","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Microsoft\u2019s AI for Good Lab","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"DOI":"10.13039\/501100004299","name":"Secretar\u00eda de Educaci\u00f3n Superior, Ciencia, Tecnolog\u00eda e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/501100004299","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Gobierno de la Rep\u00fablica del Ecuador","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Tecn\u00f3logico de Monterrey.","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer\u2019s disease (AD) can improve patient care. This study examines the role of quantitative MRI (qMRI), cognitive evaluations, apolipoprotein\n \n \n \n \n \n \u03b5<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 4 (\n APOE<\/jats:italic>\n \n \n \n \n \n \u03b5<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 4), and cerebrospinal fluid (CSF) biomarkers in Cox survival models to predict progression from MCI to AD. Data from 564 participants in the ADNI study, who transitioned from MCI to AD, were analyzed. The data set included 330 features encompassing qMRI, cognitive assessments, CSF biomarkers, and\n APOE<\/jats:italic>\n \n \n \n \n \n \u03b5<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 4 status. Advanced machine learning (ML) methods were applied to evaluate the importance of these data sources, select relevant features, and develop interpretable Cox survival models within a cross-validation framework. The top optimized model achieved a sensitivity of 0.69, 95% CI [0.63, 0.76], and a specificity of 0.87, 95% CI [0.83, 0.90], and used all data sources. The results demonstrated that combining qMRI features with cognitive assessments, CSF biomarkers, and\n APOE<\/jats:italic>\n \n \n \n \n \n \u03b5<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 4 status, analyzed using the BSWiMS model, resulted in a substantial improvement in the ability to predict progression from MCI to AD, achieving 81% precision and 87% specificity. These results exceed those obtained with other models evaluated. Finally, biomarker analysis showed that cognitive scores are the most relevant features to predict conversion, followed by CSF and qMRI biomarkers. These findings highlight the value of integrating multiple data sources in highly interpretable Cox survival models for the early identification of individuals at risk for AD.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0321671","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:36:45Z","timestamp":1777570605000},"page":"e0321671","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer\u2019s disease via data fusion and machine learning"],"prefix":"10.1371","volume":"21","author":[{"given":"Diana Sofia","family":"Rosales-Gurmendi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2041-8056","authenticated-orcid":true,"given":"Gerardo Alejandro","family":"Fumagal-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7196-3977","authenticated-orcid":true,"given":"Jorge","family":"Orozco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Farber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Trevi\u00f1o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Martinez-Ledesma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Martinez-Torteya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabiola","family":"Rosales-Gurmendi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Tamez-Pe\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"issue":"3","key":"pone.0321671.ref001","first-page":"367","article-title":"Alzheimer\u2019s disease facts and figures","volume":"14","author":"Association A","year":"2018","journal-title":"Alzheimer\u2019s & Dementia"},{"issue":"3","key":"pone.0321671.ref002","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jalz.2011.03.003","article-title":"Toward defining the preclinical stages of Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"RA Sperling","year":"2011","journal-title":"Alzheimers Dement"},{"issue":"1","key":"pone.0321671.ref003","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of Alzheimer\u2019s disease","volume":"14","author":"MA DeTure","year":"2019","journal-title":"Mol Neurodegener"},{"issue":"3","key":"pone.0321671.ref004","doi-asserted-by":"crossref","first-page":"371","DOI":"10.14283\/jpad.2021.23","article-title":"Diagnosis of Early Alzheimer\u2019s Disease: Clinical Practice in 2021","volume":"8","author":"AP Porsteinsson","year":"2021","journal-title":"J Prev Alzheimers Dis"},{"issue":"1","key":"pone.0321671.ref005","article-title":"Alzheimer\u2019s disease and related dementias diagnosis: a biomarkers meta-analysis of general and CNS extracellular vesicles","volume":"1","author":"HB Taha","year":"2025","journal-title":"npj Dement"},{"issue":"1","key":"pone.0321671.ref006","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s13311-021-01146-y","article-title":"Neuropathology of Alzheimer\u2019s Disease","volume":"19","author":"JA Trejo-Lopez","year":"2022","journal-title":"Neurotherapeutics"},{"key":"pone.0321671.ref007","doi-asserted-by":"crossref","first-page":"685636","DOI":"10.3389\/fneur.2022.685636","article-title":"The Clinical Course of Early and Late Mild Cognitive Impairment","volume":"13","author":"S-Y Lin","year":"2022","journal-title":"Front Neurol"},{"key":"pone.0321671.ref008","doi-asserted-by":"crossref","unstructured":"Orozco-Sanchez J, Tamez-Pe\u00f1a J. 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