Quantification of glioblastoma mass effect by lateral ventricle displacement

Sci Rep. 2018 Feb 12;8(1):2827. doi: 10.1038/s41598-018-21147-w.

Abstract

Mass effect has demonstrated prognostic significance for glioblastoma, but is poorly quantified. Here we define and characterize a novel neuroimaging parameter, lateral ventricle displacement (LVd), which quantifies mass effect in glioblastoma patients. LVd is defined as the magnitude of displacement from the center of mass of the lateral ventricle volume in glioblastoma patients relative to that a normal reference brain. Pre-operative MR images from 214 glioblastoma patients from The Cancer Imaging Archive (TCIA) were segmented using iterative probabilistic voxel labeling (IPVL). LVd, contrast enhancing volumes (CEV) and FLAIR hyper-intensity volumes (FHV) were determined. Associations with patient survival and tumor genomics were investigated using data from The Cancer Genome Atlas (TCGA). Glioblastoma patients had significantly higher LVd relative to patients without brain tumors. The variance of LVd was not explained by tumor volume, as defined by CEV or FLAIR. LVd was robustly associated with glioblastoma survival in Cox models which accounted for both age and Karnofsky's Performance Scale (KPS) (p = 0.006). Glioblastomas with higher LVd demonstrated increased expression of genes associated with tumor proliferation and decreased expression of genes associated with tumor invasion. Our results suggest LVd is a quantitative measure of glioblastoma mass effect and a prognostic imaging biomarker.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Brain / pathology
  • Brain Neoplasms / pathology
  • Cohort Studies
  • Female
  • Glioblastoma / pathology*
  • Heart Ventricles / pathology
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Lateral Ventricles / pathology*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Neuroimaging / methods
  • Prognosis
  • Proportional Hazards Models