{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:48:44Z","timestamp":1768816124050,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008005","name":"Yonsei University College of Medicine","doi-asserted-by":"publisher","award":["2024-32-0050"],"award-info":[{"award-number":["2024-32-0050"]}],"id":[{"id":"10.13039\/501100008005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic\u2013clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.<\/jats:p>","DOI":"10.3390\/jcm13175080","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T09:26:51Z","timestamp":1724750811000},"page":"5080","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features"],"prefix":"10.3390","volume":"13","author":[{"given":"Kyung Min","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0796-4043","authenticated-orcid":false,"given":"Bo Kyu","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"},{"name":"Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1188-449X","authenticated-orcid":false,"given":"Woo-Seok","family":"Ha","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4723-8975","authenticated-orcid":false,"given":"Soomi","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min Kyung","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoung","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5850-010X","authenticated-orcid":false,"given":"Won-Joo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1111\/j.1528-1157.1994.tb05932.x","article-title":"The prevalence and incidence of convulsive disorders in children","volume":"35","author":"Hauser","year":"1994","journal-title":"Epilepsia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1001\/archneur.1993.00540060034013","article-title":"Juvenile myoclonic epilepsy: A review","volume":"50","author":"Panayiotopoulos","year":"1993","journal-title":"Arch. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Proposal for classification of epilepsies and epileptic syndromes (1985). Commission on classification and terminology of the international league against epilepsy. Epilepsia, 26, 268\u2013278.","DOI":"10.1111\/j.1528-1157.1985.tb05417.x"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.seizure.2017.05.011","article-title":"Juvenile myoclonic epilepsy as a spectrum disorder: A focused review","volume":"49","author":"Baykan","year":"2017","journal-title":"Seizure"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S18","DOI":"10.1016\/j.yebeh.2012.06.036","article-title":"Lifetime prognosis of juvenile myoclonic epilepsy","volume":"28","author":"Baykan","year":"2013","journal-title":"Epilepsy Behav."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.seizure.2021.01.005","article-title":"Long-term prognosis of juvenile myoclonic epilepsy: A systematic review searching for sex differences","volume":"86","author":"Giuliano","year":"2021","journal-title":"Seizure"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fayad, C., Saad, K., Kahwagi, G.J., Hallit, S., Griffin, D., Abou-Khalil, R., and El-Hayek, E. (2024). A systematic review and meta-analysis of factors related to first line drugs refractoriness in patients with juvenile myoclonic epilepsy (jme). PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0300930"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1111\/ene.13811","article-title":"Refractory juvenile myoclonic epilepsy: A meta-analysis of prevalence and risk factors","volume":"26","author":"Stevelink","year":"2019","journal-title":"Eur. J. Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.seizure.2010.10.004","article-title":"Prognosis of juvenile myoclonic epilepsy is related to endophenotypes","volume":"20","author":"Guaranha","year":"2011","journal-title":"Seizure"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"fcad182","DOI":"10.1093\/braincomms\/fcad182","article-title":"Variation in prognosis and treatment outcome in juvenile myoclonic epilepsy: A biology of juvenile myoclonic epilepsy consortium proposal for a practical definition and stratified medicine classifications","volume":"5","author":"Rubboli","year":"2023","journal-title":"Brain Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Domin, M., Bartels, S., Geithner, J., Wang, Z.I., Runge, U., Grothe, M., Langner, S., and von Podewils, F. (2018). Juvenile myoclonic epilepsy shows potential structural white matter abnormalities: A tbss study. Front. Neurol., 9.","DOI":"10.3389\/fneur.2018.00509"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1111\/j.1528-1167.2012.03544.x","article-title":"Microstructural white matter abnormality and frontal cognitive dysfunctions in juvenile myoclonic epilepsy","volume":"53","author":"Kim","year":"2012","journal-title":"Epilepsia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107397","DOI":"10.1016\/j.eplepsyres.2024.107397","article-title":"Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy","volume":"205","author":"Yu","year":"2024","journal-title":"Epilepsy Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109736","DOI":"10.1016\/j.yebeh.2024.109736","article-title":"Artificial intelligence\/machine learning for epilepsy and seizure diagnosis","volume":"155","author":"Han","year":"2024","journal-title":"Epilepsy Behav."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.eplepsyres.2024.107404","article-title":"Predicting efficacy of antiseizure medication treatment with machine learning algorithms in north indian population","volume":"205","author":"Kaushik","year":"2024","journal-title":"Epilepsy Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1001\/jamaneurol.2022.2514","article-title":"Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy","volume":"79","author":"Hakeem","year":"2022","journal-title":"JAMA Neurol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3349\/ymj.2012.53.3.477","article-title":"The localizing and lateralizing value of auras in lesional partial epilepsy patients","volume":"53","author":"Ye","year":"2012","journal-title":"Yonsei Med. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.neuroimage.2004.03.032","article-title":"A hybrid approach to the skull stripping problem in mri","volume":"22","author":"Dale","year":"2004","journal-title":"Neuroimage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TMI.2006.887364","article-title":"Geometrically accurate topology-correction of cortical surfaces using nonseparating loops","volume":"26","author":"Pacheco","year":"2007","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/RBME.2022.3185953","article-title":"Explainable artificial intelligence methods in combating pandemics: A systematic review","volume":"16","author":"Giuste","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3349\/ymj.2023.0047","article-title":"A radiomics-based model for potentially more accurate identification of subtypes of breast cancer brain metastases","volume":"64","author":"Cho","year":"2023","journal-title":"Yonsei Med. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1002\/acn3.51255","article-title":"Trait impulsivity in juvenile myoclonic epilepsy","volume":"8","author":"Shakeshaft","year":"2021","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"853","DOI":"10.3174\/ajnr.A7911","article-title":"Predicting drug treatment outcomes in childrens with tuberous sclerosis complex-related epilepsy: A clinical radiomics study","volume":"44","author":"Hu","year":"2023","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.3348\/kjr.2022.0539","article-title":"Development and validation of mri-based radiomics models for diagnosing juvenile myoclonic epilepsy","volume":"23","author":"Kim","year":"2022","journal-title":"Korean J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3349\/ymj.2022.0539","article-title":"Retention rates and successful treatment with antiseizure medications in newly-diagnosed epilepsy patients","volume":"65","author":"Hwang","year":"2024","journal-title":"Yonsei Med. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7907887","DOI":"10.1155\/2023\/7907887","article-title":"Altered cerebellar volumes and intrinsic cerebellar network in juvenile myoclonic epilepsy","volume":"2023","author":"Lee","year":"2023","journal-title":"Acta Neurol. Scand."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/ane.13138","article-title":"Alteration of cerebello-thalamocortical spontaneous low-frequency oscillations in juvenile myoclonic epilepsy","volume":"140","author":"Kim","year":"2019","journal-title":"Acta Neurol. Scand."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.yebeh.2011.05.018","article-title":"Structural abnormalities of the thalamus in juvenile myoclonic epilepsy","volume":"21","author":"Mory","year":"2011","journal-title":"Epilepsy Behav."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1212\/01.wnl.0000233886.55203.bd","article-title":"Mri reveals structural abnormalities in patients with idiopathic generalized epilepsy","volume":"67","author":"Betting","year":"2006","journal-title":"Neurology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1093\/brain\/aws296","article-title":"Abnormal thalamocortical structural and functional connectivity in juvenile myoclonic epilepsy","volume":"135","author":"Vollmar","year":"2012","journal-title":"Brain"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gauffin, H., Landtblom, A.-M., Vigren, P., Frick, A., Engstr\u00f6m, M., McAllister, A., and Karlsson, T. (2022). Similar profile and magnitude of cognitive impairments in focal and generalized epilepsy: A pilot study. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.746381"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/17\/5080\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:36Z","timestamp":1760111016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/17\/5080"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,27]]},"references-count":32,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["jcm13175080"],"URL":"https:\/\/doi.org\/10.3390\/jcm13175080","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,27]]}}}