Abstract 7440: Natural language processing algorithms allow retrospective glioblastoma phenotyping by identifying true wild-type isocitrate dehydrogenase gliomas in electronic health records

Abstract                                   Background:                   Glioblastoma (GBM), the most common primary brain tumor in adults with a devastating median overall survival time of 12 months, was reclassified by the World Health Organization in 2021 to only include gliomas with wild-type isocitrate dehydrogenase (IDHwt). This presents a challenge in studying retrospective cohorts of patients diagnosed with outdated definitions of GBM, which may include tumors with IDH mutations. To address this, we developed and validated natural language processing-based models in electronic health record (EHR) data designed to identify patients with IDHwt gliomas. We also developed models for identifying important prognostic markers in GBM, such as the methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) promoter, to further demonstrate the utility of these tools and EHR data in studying GBM.                                                   Methods:                   We manually adjudicated 1, 500 pathology documents from Northwestern Medicine (NM) containing the case-insensitive word, “glioblastoma”, for the presence of a diagnosis of IDHwt glioma as well as the methylation status of MGMT. In parallel, we implemented the National Library of Medicine’s MetaMap tool on these documents to identify biomedical concepts contained within the text. These biomedical concepts were used as features in training a series of regularized logistic regression models that identify the IDH mutation and MGMT methylation status. We next constructed Kaplan-Meier survival curves, stratifying patients by MGMT methylation. Finally, we evaluated the performance of these models at Loyola University Medical Center (LUMC) to determine their efficacy at a secondary healthcare institution.                                                   Results:                   The best performing model in identifying IDHwt gliomas had an F1-measure of 0.88 in the holdout test set of documents from NM. Comparing patients with hyper-methylated and unmethylated MGMT showed significant differences in median overall survival times regardless of the MGMT identification method used (p &amp;lt; 0.001). Finally, the best performing IDHwt glioma identification model had an F1-measure of 0.96 when implemented at LUMC. 15% of patients identified at LUMC were Hispanic or Latino, compared to only 5% at NM (p &amp;lt; 0.05).                                                   Discussion:                   Our results suggest that we successfully designed and implemented models to identify patients with true IDHwt gliomas. Importantly, these models displayed excellent performance at a secondary healthcare institution, demonstrating that they can be utilized to identify ethnically diverse and multi-site GBM cohorts. Furthermore, our characterization of the NM GBM cohort recapitulated known trends in survival patterns, serving as a secondary validation metric and demonstrating the utility of EHR data in studying GBM in clinical settings.                                                   Citation Format:                   Vijeeth Guggilla, Noah Forrest, April Bell, Khizar Nandoliya, Ethan Houskamp, Steven Tran, Rimas Lukas, Jodi Johnson, Ishan Roy, Derek Wainwright, Theresa Walunas. Natural language processing algorithms allow retrospective glioblastoma phenotyping by identifying true wild-type isocitrate dehydrogenase gliomas in electronic health records [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7440.