{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:22:26Z","timestamp":1787167346916,"version":"build-2736575974"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    The objective of the study was to investigate whether radiomics features of extrahippocampal regions differ between patients with epilepsy and healthy controls, and whether any differences can identify patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    Data from 36 patients with hippocampal sclerosis (HS) and 50 healthy controls were used to construct a radiomics model. A total of 1,618 radiomics features from the affected hippocampal and extrahippocampal regions were compared with features from healthy controls and the unaffected side of patients. Using a stepwise selection method with a univariate\n                    <jats:italic>t<\/jats:italic>\n                    -test and elastic net penalization, significant predictors for identifying TLE were separately selected for the hippocampus (H+) and extrahippocampal region (H\u2013). Each model was independently validated with an internal set of MRI-negative adult TLE patients (\n                    <jats:italic>n<\/jats:italic>\n                    = 22) and pediatric validation cohort with MRI-negative TLE (\n                    <jats:italic>n<\/jats:italic>\n                    = 20) from another tertiary center; diagnostic performance was calculated using area under the curve (AUC) of the receiver-operating-characteristic curve analysis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Forty-eight significant H+ radiomic features and 99 significant H\u2013 radiomic features were selected from the affected side of patients and used to create a hippocampus model and an extrahippocampal model, respectively. Texture features were the most frequently selected feature. Training set showed slightly higher accuracy between hippocampal (AUC = 0.99) and extrahippocampal model (AUC = 0.97). In the internal validation and external validation sets, the extrahippocampal model (AUC = 0.80 and 0.92, respectively) showed higher diagnostic performance for identifying the affected side of patients than the hippocampus model (AUC = 0.67 and 0.69).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Significance:<\/jats:bold>\n                    Radiomics revealed extrahippocampal abnormality in the affected side of patients with TLE and could potentially help to identify MRI-negative TLE.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Classification of Evidence:<\/jats:bold>\n                    Class IV Criteria for Rating Diagnostic Accuracy Studies.\n                  <\/jats:p>","DOI":"10.3389\/fneur.2021.706576","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:53:32Z","timestamp":1628038412000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Extrahippocampal Radiomics Analysis Can Potentially Identify Laterality in Patients With MRI-Negative Temporal Lobe Epilepsy"],"prefix":"10.3389","volume":"12","author":[{"given":"E-Nae","family":"Cheong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji Eun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da Eun","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo Hyun","family":"Shim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1111\/epi.13699","article-title":"MRI-negative temporal lobe epilepsy-What do we know?","volume":"58","author":"Muhlhofer","year":"2017","journal-title":"Epilepsia."},{"key":"B2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.nicl.2015.07.010","article-title":"Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: a multimodal study","volume":"9","author":"Pustina","year":"2015","journal-title":"Neuroimage Clin."},{"key":"B3","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1016\/j.nicl.2016.05.002","article-title":"Combined (18)F-FDG-PET and diffusion tensor imaging in mesial temporal lobe epilepsy with hippocampal sclerosis","volume":"12","author":"Aparicio","year":"2016","journal-title":"NeuroImage. 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