Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone.

BACKGROUND

Glioblastoma (GBM) is the most common malignant central nervous system tumor, and promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to status, to improve the ability to predict prognosis.

METHODS

159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of status with radiomics was also investigated and all results were validated on the independent test set.

RESULTS

LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005).

CONCLUSION

Our results suggest that combining radiomics with is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated : one with a similar survival to unmethylated patients and the other with a significantly longer OS.