{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:56:57Z","timestamp":1782547017294,"version":"3.54.5"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aimed to investigate the role of cerebellar magnetic resonance imaging (MRI) features in identifying mild cognitive impairment (MCI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective multicenter study included patients with MCI, patients with Alzheimer's disease (AD), and healthy controls (HCs) from three tertiary hospitals in China (January 2022\u2013December 2023). Cerebellar and hippocampal radiomics features were extracted from T1-, T2-, and T2-FLAIR-weighted MRI. A sparse representation classifier was developed using 10-fold cross-validation and was validated on independent datasets. Diagnostic performance was assessed through sensitivity, specificity, and ROC-AUC values.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 87 patients with MCI, 109 patients with AD, and 55 healthy controls (HCs) matched by gender and age were included for model construction and validation. Additionally, 13 patients with MCI and 26 patients with AD were included for external validation. The 10-fold cross-validation accuracy and ROC AUC for identifying cognitive impairment (CI) in the training set using a combination of cerebellar T1, T2, and T2-FLAIR weighted images were better than those of hippocampal models (91.0% vs. 86.8%, 0.943 vs. 0.931). The accuracy and ROC AUC in the independent test set were similar (89.3% vs. 89.3%, 0.908 vs. 0.906). The 10-fold cross-validation accuracy and ROC AUC for identifying MCI in the training set, using a combination of cerebellar T1, T2, and T2-FLAIR weighted images, were similar to those of hippocampal models (85.2% vs. 83.7%, 0.877 vs. 0.905). Furthermore, the results were consistent with the external validation set (89.7% vs. 93.1%, 0.962 vs. 0.974).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Cerebellar MRI radiomics models exhibit diagnostic accuracy comparable to hippocampal models for identifying CI and MCI, supporting the cerebellum's role in detecting early cognitive dysfunction. These findings provide novel insights into cerebellar contributions to AD pathophysiology and offer potential biomarkers for clinical application.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2025.1566247","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T05:24:59Z","timestamp":1752211499000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Cerebellar MRI-based radiomics models for identifying mild cognitive impairment: a retrospective multicenter study in Southeast China"],"prefix":"10.3389","volume":"17","author":[{"given":"Jianping","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoen","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naian","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunmu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinyong","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-svd: an algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. 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