{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:58:05Z","timestamp":1790675885178,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2406900"],"award-info":[{"award-number":["2022YFC2406900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2406904"],"award-info":[{"award-number":["2022YFC2406904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Structural magnetic resonance imaging (MRI) is fundamental to presurgical localization in epilepsy, but subtle epilepsy\u2010related abnormalities may not always be apparent on routine review. This study aimed to develop and externally validate an MRI\u2010only graph attention transformer for ranking resection\u2010related cortical candidates and to evaluate its reader\u2010level utility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Graph Attention Transformer for Epilepsy\u2010Related Candidate Zones (GATEZ) was developed using preoperative three\u2010dimensional T1\u2010weighted MRI from the publicly released IDEAS (Imaging Database for Epilepsy and Surgery) database. Participants with a 12\u2010month International League Against Epilepsy class 1 outcome were split into training, validation, and internal test sets (\n                      <jats:italic>n<\/jats:italic>\n                      =\u2009171\/37\/37). Each participant was represented as a 1000\u2010parcel cortical graph with five regional morphometric features and an individualized Morphometric Inverse Divergence network; postoperative resection masks served as the surgical reference standard for model supervision. External validation used 183 consecutive surgical participants with epilepsy who underwent hybrid\n                      <jats:sup>18<\/jats:sup>\n                      F\u2010fluorodeoxyglucose (\n                      <jats:sup>18<\/jats:sup>\n                      F\u2010FDG) positron emission tomography (PET)\/MRI, with MRI only used for model inference. Reader\u2010level utility was evaluated in a blinded three\u2010reader study comparing MRI alone, MRI plus\n                      <jats:sup>18<\/jats:sup>\n                      F\u2010FDG PET, and MRI plus GATEZ.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the internal test cohort, GATEZ placed at least one resection\u2010overlapping parcel within the Top\u201010 ranked candidates in 92% of participants, with a mean Top\u201010 positive predictive value of 62%. Performance remained stable in the independent external cohort, with an 87% Top\u201010 hit rate and 59% mean Top\u201010 positive predictive value. Node\u2010level area under the precision\u2013recall curve was .29 internally and .27 externally, indicating stable enrichment of resection\u2010related regions among the highest ranked candidates. In the blinded reader study, MRI\u2009+\u2009GATEZ improved detection compared with MRI alone (74%\u201378% vs. 58%\u201366% across readers; adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u2009.001 for all readers) and performed similarly to MRI\u2009+\u2009FDG (78%\u201380% across readers; adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2265\u2009.34 for all readers).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>\n                      GATEZ generates a concise Top\u2010\n                      <jats:italic>K<\/jats:italic>\n                      shortlist of resection\u2010related cortical candidates and may serve as a practical second\u2010look aid for presurgical localization.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/epi.70327","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:59:56Z","timestamp":1781276396000},"page":"4689-4701","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>Magnetic resonance imaging<\/scp>\n                    \u2010based ranking of resection\u2010related cortical candidates for presurgical localization in epilepsy using a prior\u2010aware graph attention transformer"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7056-8570","authenticated-orcid":false,"given":"Chenyang","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Xuanwu Hospital Capital Medical University  Beijing China"},{"name":"Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics  Beijing China"},{"name":"Key Laboratory of Neurodegenerative Diseases Ministry of 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