Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma

// Erik N. Taylor 1 , Yao Ding 2 , Shan Zhu 1 , Eric Cheah 1 , Phillip Alexander 3,4 , Leon Lin 1 , George E. Aninwene II 1 , Matthew P. Hoffman 1 , Anita Mahajan 2 , Abdallah S.R. Mohamed 2 , Nathan McDannold 3 , Clifton D. Fuller 2 , Clark C. Chen 5 and Richard J. Gilbert 1 1 Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA 2 Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA 3 Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA 4 Department of Engineering Science, University of Oxford, Oxford, UK 5 Center for Theoretical and Applied Neuro-Oncology and Department of Neurosurgery, University of California, San Diego, CA, USA Correspondence to: Richard J. Gilbert, email: // Keywords : glioma, diffusion weighted MRI, cancer biomarkers Received : October 06, 2016 Accepted : January 24, 2017 Published : March 16, 2017 Abstract While it is recognized that the overall resistance of glioblastoma to treatment may be related to intra-tumor patterns of structural heterogeneity, imaging methods to assess such patterns remain rudimentary. Methods: We utilized a generalized Q-space imaging (GQI) algorithm to analyze magnetic resonance imaging (MRI) derived from a rodent model of glioblastoma and 2 clinical datasets to correlate GQI, histology, and survival. Results: In a rodent glioblastoma model, GQI demonstrated a poorly coherent core region, consisting of diffusion tracts <5 mm, surrounded by a shell of highly coherent diffusion tracts, 6-25 mm. Histologically, the core region possessed a high degree of necrosis, whereas the shell consisted of organized sheets of anaplastic cells with elevated mitotic index. These attributes define tumor architecture as the macroscopic organization of variably aligned tumor cells. Applied to MRI data from The Cancer Imaging Atlas (TCGA), the core-shell diffusion tract-length ratio (c/s ratio) correlated linearly with necrosis, which, in turn, was inversely associated with survival ( p = 0.00002). We confirmed in an independent cohort of patients ( n = 62) that the c/s ratio correlated inversely with survival ( p = 0.0004). Conclusions: The analysis of MR images by GQI affords insight into tumor architectural patterns in glioblastoma that correlate with biological heterogeneity and clinical outcome.