Impact of perfusion map analysis on early survival prediction accuracy in glioma patients.
Studies investigating dynamic susceptibility contrast magnetic resonance imaging-determined relative cerebral blood volume (rCBV) maps as a metric of treatment response assessment have generated conflicting results. We evaluated the potential of various analytical techniques to predict survival of patients with glioma treated with chemoradiation. rCBV maps were acquired in patients with high-grade gliomas at 0, 1, and 3 weeks into chemoradiation therapy. Various analytical techniques were applied to the same cohort of serial rCBV data for early assessment of survival. Three different methodologies were investigated: 1) percentage change of whole tumor statistics (i.e., mean, median, and percentiles), 2) physiological segmentation (low rCBV, medium rCBV, or high rCBV), and 3) a voxel-based approach, parametric response mapping (PRM). All analyses were performed using the same tumor contours, which were determined using contrast-enhanced T1-weighted and fluid attenuated inversion recovery images. The predictive potential of each response metric was assessed at 1-year and overall survival. PRM was the only analytical approach found to generate a response metric significantly predictive of patient 1-year survival. Time of acquisition and contour volume were not found to alter the sensitivity of the PRM approach for predicting overall survival. We have demonstrated the importance of the analytical approach in early response assessment using serial rCBV maps. The PRM analysis shows promise as a unified early and robust imaging biomarker of treatment response in patients diagnosed with high-grade gliomas.
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Benjamin
B
Lemasson
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Thomas L
TL
Chenevert
Department of Radiation Oncology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Theodore S
TS
Lawrence
Department of Radiation Oncology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Christina
C
Tsien
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI ; Department of Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden.
Pia C
PC
Sundgren
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI ; Department of Biomedical Engineering, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Charles R
CR
Meyer
Department of Neurology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Larry
L
Junck
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Jennifer
J
Boes
Department of Radiation Oncology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Stefanie
S
Galbán
Department of Biostatistics, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Timothy D
TD
Johnson
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI ; Department of Radiation Oncology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Alnawaz
A
Rehemtulla
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Brian D
BD
Ross
Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.
Craig J
CJ
Galbán