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

Erik N. Taylor, Yao Ding, Shan Zhu, Eric Cheah, Phillip Alexander, Leon Lin, George E. Aninwene, Matthew P. Hoffman, Anita Mahajan, Abdallah S.R. Mohamed, Nathan McDannold, Clifton D. Fuller, Clark C. Chen, Richard J. Gilbert

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)41815-41826
Number of pages12
JournalOncotarget
Volume8
Issue number26
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© Taylor et al.

Keywords

  • Cancer biomarkers
  • Diffusion weighted MRI
  • Glioma

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