ArticleNeuro-oncology advances
Sociodemographic factors predict outcomes and reveal spatial tumor patterns in glioblastoma.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
15 authors.
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Abstract
Background: 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. Methods: Glioblastoma patients ( Results: 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. Conclusions: 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.
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