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Updated: Sep 14, 2025
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Natural language processing algorithms identify wild-type isocitrate dehydrogenase gliomas in electronic health
Noah Forrest1, Vijeeth Guggilla1, April Bell1
1Institute for Artificial Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Background:
In 2021, the World Health Organization reclassified glioblastomas to include only gliomas with wild-type isocitrate dehydrogenase (IDHwt). Reclassification has created a challenge for retrospective identification of patients with GBM, as many were classified with outdated definitions. This study aims to address this challenge by using natural language processing (NLP) on electronic health record (EHR) data to identify patients with wild-type IDH glioma.
Methods:
We manually adjudicated a subset of 1499 pathology records for evidence of IDHwt glioma as well as the methylation status of the MGMT promoter. We then trained several regularized logistic regression models that identify the IDH mutation and MGMT promoter status using biomedical concepts identified in the text. These models were then validated at a second site. Kaplan-Meier curves stratifying patients by their MGMT promoter methylation status and other clinical variables were constructed for further cohort characterization.
Results:
The best-performing model for identifying IDHwt glioma displayed an F1 measure of 0.88. Comparing patients with methylated and unmethylated MGMT promoter showed significant differences in median overall survival times (P < 0.001). Finally, the best-performing IDHwt glioma identification model displayed an F1 measure of 0.962 when implemented at a secondary site.
Discussion:
Our results suggest that we can identify patients with IDHwt glioma in pathology notes in the EHR using NLP. Our models displayed excellent performance at a secondary healthcare institution, demonstrating that they can identify multi-site GBM cohorts. Furthermore, our characterization of the NM GBM cohort recapitulated known survival trends, demonstrating the utility of EHR data in studying GBM in clinical settings.
