{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:25:10Z","timestamp":1782818710927,"version":"3.54.5"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003196","name":"Italian Ministry of Health","doi-asserted-by":"publisher","award":["RF-2021-12374301"],"award-info":[{"award-number":["RF-2021-12374301"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003196","name":"Italian Ministry of Health","doi-asserted-by":"publisher","award":["GR-2021-12374417"],"award-info":[{"award-number":["GR-2021-12374417"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Alzheimer\u2019s disease is a disabling neurodegenerative disorder for which no effective treatment currently exists. To predict the diagnosis of Alzheimer\u2019s disease could be crucial for patients\u2019 outcome, but current Alzheimer\u2019s disease biomarkers are invasive, time consuming or expensive. Thus, developing MRI-based computational methods for Alzheimer\u2019s disease early diagnosis would be essential to narrow down the phenotypic measures predictive of cognitive decline. Amnestic mild cognitive impairment (aMCI) is associated with higher risk for Alzheimer\u2019s disease, and here, we aimed to identify MRI-based quantitative rules to predict aMCI to possible Alzheimer\u2019s disease conversion, applying different machine learning algorithms sequentially. At baseline, T1-weighted brain images were collected for 104 aMCI patients and processed to obtain 146 volumetric measures of cerebral grey matter regions [regions of interest (ROIs)]. One year later, patients were classified as converters (aMCI-c = 32) or non-converters, i.e. clinically and neuropsychologically stable (aMCI-s = 72) based on cognitive performance. Feature selection was performed by random forest (RF), and the identified seven ROIs volumetric data were used to implement support vector machine (SVM) and decision tree (DT) classification algorithms. Both SVM and DT reached an average accuracy of 86% in identifying aMCI-c and aMCI-s. DT found a critical threshold volume of the right entorhinal cortex (EC-r) as the first feature for differentiating aMCI-c\/aMCI-s. Almost all aMCI-c had an EC-r volume &amp;lt;1286\u2005mm3, while more than half of the aMCI-s patients had a volume above the identified threshold for this structure. Other key regions for the classification between aMCI-c\/aMCI-s were the left lateral occipital (LOC-l), the middle temporal gyrus and the temporal pole cortices. Our study reinforces previous evidence suggesting that the morphometry of the EC-r and LOC-l best predicts aMCI to Alzheimer\u2019s disease conversion. Further investigations are needed prior to deeming our findings as a broadly applicable predictive framework. However, here, a first indication was derived for volumetric thresholds that, being easy to obtain, may assist in early identification of Alzheimer\u2019s disease in clinical practice, thus contributing to establishing MRI as a useful non-invasive prognostic instrument for dementia onset.<\/jats:p>","DOI":"10.1093\/braincomms\/fcaf027","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T08:32:46Z","timestamp":1737448366000},"source":"Crossref","is-referenced-by-count":5,"title":["Evaluating conversion from mild cognitive impairment to Alzheimer\u2019s disease with structural MRI: a machine learning study"],"prefix":"10.1093","volume":"7","author":[{"given":"Daniela","family":"Vecchio","sequence":"first","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federica","family":"Piras","sequence":"additional","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federica","family":"Natalizi","sequence":"additional","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]},{"name":"Department of Psychology, \u2018Sapienza\u2019 University of Rome , Rome 00185 ,","place":["Italy"]},{"name":"PhD Program in Behavioral Neuroscience, Sapienza University of Rome , Rome 00161 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nerisa","family":"Banaj","sequence":"additional","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clelia","family":"Pellicano","sequence":"additional","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabrizio","family":"Piras","sequence":"additional","affiliation":[{"name":"Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation , Rome 00179 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"issue":"4","key":"2025013014365778300_fcaf027-B1","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1002\/alz.13016","article-title":"2023 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