{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:14:27Z","timestamp":1789434867479,"version":"build-2803163510"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":167,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007631","name":"Canadian Institute for Advanced Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007631","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Temerty Center for Artificial Intelligence Research and Education in Medicine"},{"name":"University of Toronto Baden Havard Endowment fund"},{"DOI":"10.13039\/501100021202","name":"Digital Research Alliance of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021202","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Chronic pain remains a challenge for clinicians, with limited individualized predictive tools that can aid with diagnosis, disease course, or prediction of treatment outcomes. We hypothesized that a comprehensive analysis, encompassing a patient's complete pain-related clinical data, medical history and brain imaging, can identify key contributors linked to surgical outcomes and stratify specific outcome categories for trigeminal neuralgia (TN)\u2014chronic facial pain syndrome. Using supervised and unsupervised machine learning approaches, we analysed data from 102 subjects with classical TN. Pre-surgical clinical data were processed through unsupervised learning to delineate key clinical contributors of TN outcome stratification and their correlation with surgical response. Concurrently, we applied supervised learning to pre-surgical T1-weighted brain magnetic resonance imaging. Clinical data analysis uncovered pain and non-pain-related measures\u2014including pain frequency, degree of medication relief, pain character, presence of diabetes and cancer history\u2014as the most significant in forecasting surgical outcome. Analysis revealed strong correlation of pre-surgical clinical data with surgical response duration (r = 0.5, P &amp;lt; 0.00001). Imaging data analysis used a support vector machine classification model with high recall for subjects who would be either long-term responders or non-responders 0.79 and 0.86 with the area under the receiver operating characteristic curve (AUC) of 0.86 and 0.84, respectively. The average multiclass accuracy in predicting the duration of surgical response categories was 78% (AUC 0.8). Together, these results show that TN surgical outcome categories are distinguishable, and surgical outcome can be stratified based on combined clinical and brain imaging data available prior to surgical treatment. We suggest a novel perspective on different strata of chronic pain disorders, each with structural imaging, clinical correlates and specific surgical outcomes.<\/jats:p>","DOI":"10.1093\/braincomms\/fcaf178","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T18:15:02Z","timestamp":1750961702000},"source":"Crossref","is-referenced-by-count":3,"title":["Stratification of surgical outcomes in trigeminal neuralgia using multimodal data"],"prefix":"10.1093","volume":"7","author":[{"given":"Timur H","family":"Latypov","sequence":"first","affiliation":[{"name":"Krembil Research Institute, University Health Network , Toronto, Ontario , , M5T 0S8","place":["Canada"]},{"name":"Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto , Toronto, Ontario , , M5S 3K3","place":["Canada"]},{"name":"Collaborative Program in Neuroscience, University of Toronto , Toronto, Ontario , , M5S 3K3","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rose","family":"Yakubov","sequence":"additional","affiliation":[{"name":"MD Program, Temerty Faculty of Medicine, University of Toronto , Toronto, Ontario , , M5S 3K3","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"J\u00f6rgens","sequence":"additional","affiliation":[{"name":"Krembil Research Institute, University Health Network , Toronto, Ontario , , M5T 0S8","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6897-6331","authenticated-orcid":false,"given":"Pascale","family":"Tsai","sequence":"additional","affiliation":[{"name":"Michael G. 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