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. 2016 Dec;18(12):1680-1687.
doi: 10.1093/neuonc/now086. Epub 2016 May 4.

Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

Ken Chang  1 , Biqi Zhang  1 , Xiaotao Guo  1 , Min Zong  1 , Rifaquat Rahman  1 , David Sanchez  1 , Nicolette Winder  1 , David A Reardon  1 , Binsheng Zhao  1 , Patrick Y Wen  1 , Raymond Y Huang  1
Affiliations

Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

Ken Chang et al. Neuro Oncol. 2016 Dec.

Abstract

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.

Keywords: bevacizumab; glioblastoma; machine learning; recurrent; survival.

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Figures

Fig. 1.
Fig. 1.
Flow chart of analysis within this study, displaying image processing, feature extraction, machine learning, and validation steps.
Fig. 2.
Fig. 2.
Kaplan–Meier survival curves for predicted long and short survivors for (a) pretherapy features predicting PFS (training set: HR 18.27, P < .001; testing set: HR 1.37, P = .38), (b) pretherapy features predicting OS (training set: HR 6.03, P < .001; testing set: HR 1.77, P = .12), (c) pre- and posttherapy features predicting OS (training set: HR 5.10, P < .001; testing set: HR 3.64, P < .005), and (d) pre- and posttherapy features predicting OS excluding early progressors (training set: HR 16.12, P < .001; testing set: HR 2.02, P = .07).
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