Abstract
INTRODUCTION
Despite multimodal treatment for high-grade gliomas, prognosis remains grim. Since the development of the RTOG-RPA, high-grade gliomas have seen the widespread introduction of temozolomide and tumor onco-genetics. We aimed to determine whether the RTOG-RPA retained prognostic significance in the context of modern treatment paradigm, as well as generate an updated RPA incorporating both clinical and genetic variables.
METHODS
Patients with histologically-proven high-grade gliomas treated with IMRT between 2004–2017 were reviewed. The primary endpoint was overall survival from date of diagnosis. Primary analysis compared actual survival rates to that expected of corresponding RTOG-RPA class. Secondary analysis utilized the rpart function to recursively partition overall survival by numerous clinical and genetic pre-treatment and treatment-related variables. A tertiary analysis recursively partitioned a subset of patients in which the status of all genetic markers were known.
RESULTS
We identified 878 patients with a median overall survival of 14.2 months (95% CI: 13.1–15.3). Our cohort validated the relative prognostic ordering of the RTOG RPA survival classes except class II. Our new RPA created 7 significantly different survival classes (p2 = 584) with median survival ranging from 96.4 to 2.9 months based on age, histology, MGMT methylation, radiation fractions, tumor location, radiation dose, temozolomide, and resection. Our second RPA of our genetic subset (291 patients) generated 5 significantly different survival classes (p2 = 166) with survival ranging from 65.3 to 5.6 months based on age, IDH1 mutation, MGMT methylation, neurologic functional classification, IMRT hospitalization, temozolomide, and KPS.
CONCLUSION
This series represents a large RPA analyzing clinical and genetic factors and generated 7 distinct survival classes. Further assessment of patients with fully available genetic markers generated 5 distinct survival classes. These classifications need to be validated by a prospective dataset and compared against the RTOG-RPA to determine if they provide improved prognostic power.