{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:28:40Z","timestamp":1791894520358,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82101983"],"award-info":[{"award-number":["82101983"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop and validate a multimodal combinatorial model based on whole\u2010brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4\u2010year follow\u2010up period. All patients underwent MRI scans, and radiomic features were extracted from the whole\u2010brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver\u2010operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum\u2010related DTI parameters between the low\u2010risk and high\u2010risk groups distinguished by the model (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low\u2010risk and the high\u2010risk groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.14789","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T05:25:48Z","timestamp":1719293148000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Prediction of cognitive decline in Parkinson's disease based on <scp>MRI<\/scp> radiomics and clinical features: A multicenter 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