{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:02:06Z","timestamp":1779192126108,"version":"3.51.4"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epileptic Disorders"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aimed to differentiate temporal\u2010plus epilepsy (TPE) from temporal lobe epilepsy (TLE) using extraction of radiomics features from three\u2010dimensional magnetization\u2010prepared rapid acquisition gradient echo (3D\u2010MPRAGE) imaging data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data from patients with TLE or TPE who underwent epilepsy surgery between January 2019 and January 2021 were retrospectively analyzed. Thirty\u2010three regions of interest in the affected hemisphere of each patient were defined on 3D\u2010MPRAGE images. A total of 3531 image features were extracted from each patient. Four feature selection methods and 10 machine learning algorithms were used to build 40 differentiation models. Model performance was evaluated using receiver operating characteristic analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eighty\u2010two patients were included for analysis, 47 with TLE and 35 with TPE. The model combining logistic regression and the relief selection method had the best performance (area under the receiver operating characteristic curve, .779; accuracy, .875; sensitivity, .800; specificity, .929; positive predictive value, .889; negative predictive value, .867).<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>Radiomics analysis can differentiate TPE from TLE. The logistic regression classifier trained with radiomics features extracted from 3D\u2010MPRAGE images had the highest accuracy and best performance.<\/jats:p><\/jats:sec>","DOI":"10.1002\/epd2.20092","type":"journal-article","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T22:41:43Z","timestamp":1687646503000},"page":"681-689","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Radiomics features from <scp>3D\u2010MPRAGE<\/scp> imaging can differentiate temporal\u2010plus epilepsy from temporal lobe epilepsy"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1706-7872","authenticated-orcid":false,"given":"Fangzhao","family":"Yin","sequence":"first","affiliation":[{"name":"Beijing Institute of Functional Neurosurgery, Xuanwu Hospital Capital Medical University  Beijing China"},{"name":"Tianjin Huanhu Hospital  Tianjin 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