Prospective analysis of parametric response map-derived MRI biomarkers: identification of early and distinct glioma response patterns not predicted by standard radiographic assessment

Research output: Contribution to journalArticle


PURPOSE: Currently, radiologic response of brain tumors is assessed according to the Macdonald criteria 10 weeks from the start of therapy. There exists a critical need to identify nonresponding patients early in the course of their therapy for consideration of alternative treatment strategies. Our study assessed the effectiveness of the parametric response map (PRM) imaging biomarker to provide for an earlier measure of patient survival prediction.

EXPERIMENTAL DESIGN: Forty-five high-grade glioma patients received concurrent chemoradiation. Quantitative MRI including apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps were acquired pretreatment and 3 weeks midtreatment on a prospective institutional-approved study. PRM, a voxel-by-voxel image analysis method, was evaluated as an early prognostic biomarker of overall survival. Clinical and conventional MR parameters were also evaluated.

RESULTS: Multivariate analysis showed that PRM(ADC+) in combination with PRM(rCBV-) obtained at week 3 had a stronger correlation to 1-year and overall survival rates than any baseline clinical or treatment response imaging metric. The composite biomarker identified three distinct patient groups, nonresponders [median survival (MS) of 5.5 months, 95% CI: 4.4-6.6 months], partial responders (MS of 16 months, 95% CI: 8.6-23.4 months), and responders (MS has not yet been reached).

CONCLUSIONS: Inclusion of PRM(ADC+) and PRM(rCBV-) into a single imaging biomarker metric provided early identification of patients resistant to standard chemoradiation. In comparison to the current standard of assessment of response at 10 weeks (Macdonald criteria), the composite PRM biomarker potentially provides a useful opportunity for clinicians to identify patients who may benefit from alternative treatment strategies.


  • Craig J Galbán
  • Thomas L Chenevert
  • Charles R Meyer
  • Christina Tsien
  • Theodore S Lawrence
  • Daniel A Hamstra
  • Larry Junck
  • Pia C Sundgren
  • Timothy D Johnson
  • Stefanie Galbán
  • Judith S Sebolt-Leopold
  • Alnawaz Rehemtulla
  • Brian D Ross
External organisations
  • University of Michigan
Research areas and keywords


  • Biomarkers, Brain Neoplasms, Glioma, Humans, Kaplan-Meier Estimate, Magnetic Resonance Imaging, Middle Aged, Models, Statistical, Neoplasm Staging, Prognosis, Prospective Studies
Original languageEnglish
Pages (from-to)4751-60
Number of pages10
JournalClinical Cancer Research
Issue number14
Publication statusPublished - 2011 Jul 15
Publication categoryResearch
Externally publishedYes