{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T09:08:50Z","timestamp":1779008930193,"version":"3.51.4"},"reference-count":28,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82425024"],"award-info":[{"award-number":["82425024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372049"],"award-info":[{"award-number":["82372049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472054"],"award-info":[{"award-number":["82472054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L222078"],"award-info":[{"award-number":["L222078"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L256052"],"award-info":[{"award-number":["L256052"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park","doi-asserted-by":"publisher","award":["Z241100007724008"],"award-info":[{"award-number":["Z241100007724008"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers. Approximately 60% of patients fail to have their ETs identified through noninvasive preoperative evaluations, creating an urgent clinical need for effective, noninvasive localization strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A novel fusion model was developed, integrating lesion network mapping\u2010based risk assessment with a machine learning prediction model that utilizes brain functional connectivity and random forest algorithms. The model was built based on magnetic resonance imaging data. Retrospective analysis was conducted on patients with TSC\u2010related epilepsy who had undergone resective surgery and achieved seizure freedom at the 1\u2010year follow\u2010up; tubers were classified as true epileptogenic tubers (true ETs) or true non\u2010epileptogenic tubers (true non\u2010ETs) according to the resected regions. The model calculated and ranked the probability of each tuber being an ET for every patient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 47 patients were enrolled in the study. The fusion model successfully ranked the true ETs within the top three in 91% of the cases. Significant differences in the probability rankings of ETs were observed among true ETs, true non\u2010ETs, and random tubers (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). Receiver operating characteristic curves were plotted to evaluate the accuracy of true ET localization across different methods, and the fusion model exhibited an area under the curve of 0.86. This performance significantly outperformed that of scalp electroencephalography, semiology, and positron emission tomography based on structural magnetic resonance imaging in the same cohort. Cross\u2010validation in three independent epilepsy centers confirmed the model's high generalizability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>Overall, this fusion model demonstrates high accuracy and robust clinical utility as a noninvasive tool for the localization of ETs. It effectively addresses the current challenges in identifying ETs, providing valuable support for surgical planning in patients with TSC\u2010related epilepsy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acn3.70277","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T11:29:39Z","timestamp":1765625379000},"page":"972-983","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning"],"prefix":"10.1002","volume":"13","author":[{"given":"Tinghong","family":"Liu","sequence":"first","affiliation":[{"name":"Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital Capital Medical University  Beijing China"},{"name":"Key Laboratory of Major Diseases in Children Ministry of Education  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Brainnetcome Center and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences  Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhui","family":"Kuang","sequence":"additional","affiliation":[{"name":"Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital Capital Medical University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dezhi","family":"Cao","sequence":"additional","affiliation":[{"name":"Epilepsy Center, Neurology Department Shenzhen Children's Hospital. Shenzhen  Shenzhen Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Ding","sequence":"additional","affiliation":[{"name":"Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital Capital Medical University  Beijing China"},{"name":"Neurosurgery Department PLA General Hospital  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Neurosurgery Department PLA General Hospital  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihua","family":"Wei","sequence":"additional","affiliation":[{"name":"Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital Capital Medical University  Beijing 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