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Updated: Mar 19, 2026
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly
Sina Burth1, Philipp Kickingereder1, Oliver Eidel1
1Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (S.B., P.K., O.E., D.B., L.W., M.B., A.R.); Division of Bioststatistics, German Cancer Research Center, Heidelberg, Germany (D.T.); Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany (A.W., S.L., A.H., W.W.); Department of Neurology, Innsbruck Medical University, Innsbruck, Austria (M.N.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (H.S.).
Background:
The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (ktrans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients.
Methods:
Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS.
Results:
Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBVCE for PFS, and age, sex, KPS, and EOR for OS.
Conclusions:
MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBVCE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.
