{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:54:04Z","timestamp":1754153644949,"version":"3.41.2"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":170,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01CA203861"],"award-info":[{"award-number":["R01CA203861"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["U24NS109103"],"award-info":[{"award-number":["U24NS109103"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01EB026439 P41EB018783"],"award-info":[{"award-number":["R01EB026439 P41EB018783"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Washington University Yearlong Research Program Dean\u2019s Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Glioblastoma (GBM) is the deadliest malignant glioma of the central nervous system. Postsurgical functional impairment correlates with survival and is estimated using metrics such as extent and location of resection. This study uses machine learning to evaluate the predictive ability of baseline sociodemographic and lifestyle factors for forecasting postoperative functional outcomes in GBM patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Glioblastoma patients (N\u2005=\u2005115) from the neurosurgery brain tumor service at Washington University were retrospectively identified. All patients underwent neuroimaging, surgical resection of the GBM, and at least 3 postoperative follow-up visits. Demographic, lifestyle, and socioeconomic factors (socioeconomic status [SES] and Social Vulnerability Index [SVI]) were used to train decision tree classifiers to predict postoperative Karnofsky Performance Status (KPS\u2005\u2264\u200570, KPS\u2005&amp;gt;\u200570), as well as classify the change in KPS (KPS slope) over multiple visits (decreased\/improved or maintained constant).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Utilizing decision trees with age, SVI\/SES, sex, tobacco use, alcohol use, obesity, and race as predictors, we achieved 88% accuracy in classifying median KPS and 85% accuracy in classifying KPS slope. Socioeconomic factors were the strongest predictors. Age, sex, and tobacco use were also strong predictors. Significant correlations in spatial tumor distributions were observed based on outcome measures and SVI\/SES.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The current work demonstrates the utility of machine learning to predict functional outcomes in GBM patients prior to treatment using lifestyle and sociodemographic factors. Our results suggest that socioeconomic factors, age, tobacco use, and biological sex can be reliable predictors of functional outcomes. Incorporating these factors could improve therapeutic approaches tailored to individual patients.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/noajnl\/vdaf137","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T20:18:28Z","timestamp":1751055508000},"source":"Crossref","is-referenced-by-count":0,"title":["Sociodemographic factors predict outcomes and reveal spatial tumor patterns in glioblastoma"],"prefix":"10.1093","volume":"7","author":[{"given":"Patrick H","family":"Luckett","sequence":"first","affiliation":[{"name":"Department of Neurological Surgery, Washington University School of Medicine , St. Louis, Missouri ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Olufawo","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Washington University School of Medicine , St. Louis, Missouri 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Medicine , St. Louis, Missouri ,","place":["USA"]},{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine , St. Louis, Missouri ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua S","family":"Shimony","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine , St. Louis, Missouri ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric C","family":"Leuthardt","sequence":"additional","affiliation":[{"name":"National Center for Adaptive Neurotechnologies , Albany, New York ,","place":["USA"]},{"name":"Brain Laser Center, Washington University School of Medicine , St. Louis, Missouri ,","place":["USA"]},{"name":"Center for Innovation in Neuroscience and Technology, Washington University School of Medicine , St. Louis, Missouri ,","place":["USA"]},{"name":"Department of Mechanical Engineering and Materials Science, 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