{"doi":"10.1093/noajnl/vdaf137","title":"Sociodemographic factors predict outcomes and reveal spatial tumor patterns in glioblastoma","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 = 115) 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 ≤ 70, KPS &amp;gt; 70), 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>","journal":"Neuro-Oncology Advances","year":2025,"id":647552,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1068802,"name":"Michael Olufawo","orcid":"0009-0006-7214-0207","position":1,"is_corresponding":false},{"id":1687167,"name":"Noah Naddaff-Slocum","orcid":null,"position":2,"is_corresponding":false},{"id":310767,"name":"Ki Yun Park","orcid":"0000-0003-2413-9421","position":3,"is_corresponding":false},{"id":274785,"name":"Bidhan Lamichhane","orcid":"0000-0001-7655-4681","position":4,"is_corresponding":false},{"id":259386,"name":"Donna Dierker","orcid":"0000-0001-9791-8384","position":5,"is_corresponding":false},{"id":1687168,"name":"Gabriel Trevino Verastegui","orcid":null,"position":6,"is_corresponding":false},{"id":1687169,"name":"John J Lee","orcid":null,"position":7,"is_corresponding":false},{"id":316658,"name":"Peter Yang","orcid":"0000-0002-2118-7105","position":8,"is_corresponding":false},{"id":1091419,"name":"Albert Kim","orcid":"0000-0003-1539-1246","position":9,"is_corresponding":false},{"id":1687170,"name":"Omar H Butt","orcid":null,"position":10,"is_corresponding":false},{"id":1687171,"name":"Milan G Chheda","orcid":null,"position":11,"is_corresponding":false},{"id":1687172,"name":"Abraham Z Snyder","orcid":null,"position":12,"is_corresponding":false},{"id":1687173,"name":"Joshua S Shimony","orcid":null,"position":13,"is_corresponding":false},{"id":1358814,"name":"Eric C Leuthardt","orcid":null,"position":14,"is_corresponding":false},{"id":1687166,"name":"Patrick H Luckett","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Sociodemographic factors predict outcomes and reveal spatial tumor patterns in glioblastoma","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 = 115) 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 ≤ 70, KPS &amp;gt; 70), 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>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40703803","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Cancer Institute","grant_id":"R01CA203861","title":null},{"funder_name":"National Institute of Neurological Disorders and Stroke","grant_id":"U24NS109103","title":null},{"funder_name":"National Institute of Biomedical Imaging and Bioengineering","grant_id":"R01EB026439 P41EB018783","title":null},{"funder_name":"National Institutes of Health","grant_id":"1U24NS109103-01","title":"BCI2000: Software Resource for Adaptive Neurotechnology Research"},{"funder_name":"National Institutes of Health","grant_id":"2R01CA203861-06A1","title":"Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning"},{"funder_name":"National Institutes of Health","grant_id":"7R01EB026439-04","title":"BCI2000+: A Software Platform for Adaptive Neurotechnologies"},{"funder_name":"National Institutes of Health","grant_id":"5P41EB018783-04","title":"Center for Adaptive Neurotechnologies"},{"funder_name":"NIBIB NIH HHS","grant_id":"P41 EB018783","title":null},{"funder_name":"NIBIB NIH HHS","grant_id":"R01 EB026439","title":null}],"total_grants":9,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://academic.oup.com/noa/article-pdf/7/1/vdaf137/63536249/vdaf137.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/noa/advance-article-pdf/doi/10.1093/noajnl/vdaf137/63536249/vdaf137.pdf","host_type":"publisher"},{"url":"https://digitalcommons.wustl.edu/oa_4/5549","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12284634","host_type":"repository"},{"url":"https://doi.org/10.1093/noajnl/vdaf137","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40703803","host_type":""},{"url":"http://dx.doi.org/10.1093/noajnl/vdaf137","host_type":""}],"fields_of_study":["03 medical and health sciences","0305 other medical science"],"mesh_terms":[],"keywords":["Basic and Translational Investigations","brain tumor","glioblastoma","machine learning","sociodemographic"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T01:07:58.738445Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}