Research Papers:

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

Florent Tixier _, Hyemin Um, Dalton Bermudez, Aditi Iyer, Aditya Apte, Maya S. Graham, Kathryn S. Nevel, Joseph O. Deasy, Robert J. Young and Harini Veeraraghavan

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Oncotarget. 2019; 10:660-672. https://doi.org/10.18632/oncotarget.26578

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Florent Tixier1, Hyemin Um1, Dalton Bermudez1, Aditi Iyer1, Aditya Apte1, Maya S. Graham2, Kathryn S. Nevel2,3, Joseph O. Deasy1, Robert J. Young4,5,* and Harini Veeraraghavan1,*

1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA

2Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA

3Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA

4Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA

5Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA

*Co-senior authors

Correspondence to:

Florent Tixier, email: [email protected]

Keywords: magnetic resonance imaging; radiomics; glioblastoma; MGMT; survival analysis

Received: October 09, 2018     Accepted: December 22, 2018     Published: January 18, 2019


Background: Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT 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 MGMT 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 MGMT 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 MGMT 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 MGMT 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 MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.

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