title: Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity.
abstract: OBJECTIVE
  To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma.
  
  
  METHODS
  Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score.
  
  
  RESULTS
  The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score.
  
  
  CONCLUSIONS
  Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.
authors: From the Departments of Radiology (H.A., X.D., R.L.W., M.B., R.V., C.D.) and Neurosurgery (L.M., D.M.O.), University of Pennsylvania, 3600 Market St, Suite 380, Philadelphia, PA 19104.
authors: Hamed
authors: H
authors: Akbari
authors: Luke
authors: L
authors: Macyszyn
authors: Xiao
authors: X
authors: Da
authors: Ronald L
authors: RL
authors: Wolf
authors: Michel
authors: M
authors: Bilello
authors: Ragini
authors: R
authors: Verma
authors: Donald M
authors: DM
authors: O'Rourke
authors: Christos
authors: C
authors: Davatzikos
mesh_terms: D001932:Brain Neoplasms / Q000473:pathology*; D003287:Contrast Media; D005260:Female; D005909:Glioblastoma / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008536:Meglumine / Q000031:analogs & derivatives; D009364:Neoplasm Recurrence, Local; D009942:Organometallic Compounds; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine