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Updated: Mar 20, 2026
Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma
Published on: October 27, 2014
Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab
Ken Chang1, Biqi Zhang1, Xiaotao Guo1
1Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.).
Background:
Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab.
Methods:
The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model.
Results:
Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively.
Conclusion:
With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.
