title: Predicting survival in glioblastomas using diffusion tensor imaging metrics.
abstract: OBJECTIVE
  To retrospectively correlate various diffusion tensor imaging (DTI) metrics in patients with glioblastoma multiforme (GBM) with patient survival analysis and also degree of tumor proliferation index determined histologically.
  
  
  METHODS
  Thirty-four patients with histologically confirmed treatment naive GBMs underwent DTI on a 3.0 Tesla (T) scanner. Region-of-interest was placed on the whole lesion including the enhancing as well as nonenhancing component of the lesion to determine the various DTI metrics. Kaplan-Meier estimates and Cox proportional hazards regression methods were used to assess the relationship of DTI metrics (minimum and mean values) and Ki-67 with progression free survival (PFS). To study the relationship between DTI metrics and Ki-67, Pearson's correlation coefficient was computed.
  
  
  RESULTS
  Univariate analysis showed that patients with fractional anisotropy (FA)(mean) ≤ 0.2, apparent diffusion coefficient (ADC)(min) ≤ 0.6, planar anisotropy (CP)(min) ≤ 0.002, spherical anisotropy (CS)(mean) > 0.68 and Ki-67 > 0.3 had lower PFS rate. The multivariate analysis demonstrated that only CP(min) was the best predictor of survival in these patients, after adjusting for age, Karnofsky performance scale and extent of resection. No significant correlation between DTI metrics and Ki-67 were observed.
  
  
  CONCLUSIONS
  DTI metrics can be used as a sensitive and early indicator for PFS in patients with glioblastomas. This could be useful for treatment planning as high-grade gliomas with lower ADC(min), FA(mean), CP(min), and higher CS(mean) values may be treated more aggressively.
authors.affiliation: Department of Radiology, Henry Ford Health System, Detroit, Michigan 48202, USA.
authors.forename: Sona
authors.initials: S
authors.lastname: Saksena
authors.forename: Rajan
authors.initials: R
authors.lastname: Jain
authors.forename: Jayant
authors.initials: J
authors.lastname: Narang
authors.forename: Lisa
authors.initials: L
authors.lastname: Scarpace
authors.forename: Lonni R
authors.initials: LR
authors.lastname: Schultz
authors.forename: Norman L
authors.initials: NL
authors.lastname: Lehman
authors.forename: David
authors.initials: D
authors.lastname: Hearshen
authors.forename: Suresh C
authors.initials: SC
authors.lastname: Patel
authors.forename: Tom
authors.initials: T
authors.lastname: Mikkelsen
mesh_terms: D000368:Aged; D016880:Anisotropy; D001932:Brain Neoplasms / Q000175:diagnosis / Q000401:mortality* / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D018572:Disease-Free Survival; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis / Q000401:mortality* / Q000473:pathology*; D006801:Humans; D019394:Ki-67 Antigen / Q000096:biosynthesis; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D016896:Treatment Outcome
chemical_list: D019394:Ki-67 Antigen