The Added Prognostic Value of Preoperative Dynamic Contrast-Enhanced MRI Histogram Analysis in Patients with Glioblastoma: Analysis of Overall and Progression-Free Survival

BACKGROUND AND PURPOSE: The prognostic value of dynamic contrast-enhanced MR imaging in patients with glioblastoma is controversial. We investigated the added prognostic value of dynamic contrast-enhanced MR imaging to clinical parameters and molecular biomarkers in patients with glioblastoma by using histogram analysis. MATERIALS AND METHODS: This retrospective study consisted of 61 patients who underwent preoperative dynamic contrast-enhanced MR imaging for glioblastoma. The histogram parameters of dynamic contrast-enhanced MR imaging, including volume transfer constant, extravascular extracellular volume fraction, and plasma volume fraction, were calculated from entire enhancing tumors. Univariate analyses for overall survival and progression-free survival were performed with preoperative clinical and dynamic contrast-enhanced MR imaging parameters and postoperative molecular biomarkers. Multivariate Cox regression was performed to build pre- and postoperative models for overall survival and progression-free survival. The performance of models was assessed by calculating the Harrell concordance index. RESULTS: In univariate analysis, patients with higher volume transfer constant and extravascular extracellular volume fraction values showed worse overall survival and progression-free survival, whereas plasma volume fraction showed no significant correlation. In multivariate analyses for overall survival, the fifth percentile value of volume transfer constant and kurtosis of extravascular extracellular volume fraction were independently prognostic in the preoperative model, and kurtosis of volume transfer constant and extravascular extracellular volume fraction were independently prognostic in the postoperative model. For progression-free survival, independent prognostic factors were minimum and fifth percentile values of volume transfer constant and kurtosis of extravascular extracellular volume fraction in the preoperative model and kurtosis of extravascular extracellular volume fraction in the postoperative model. The performance of preoperative models for progression-free survival was significantly improved when minimum or fifth percentile values of volume transfer constant and kurtosis of extravascular extracellular volume fraction were added. CONCLUSIONS: Higher volume transfer constant and extravascular extracellular volume fraction values are associated with worse prognosis, and dynamic contrast-enhanced MR imaging may have added prognostic value in combination with preoperative clinical parameters, especially in predicting progression-free survival.

[1]  M Takahashi,et al.  Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. , 1998, AJR. American journal of roentgenology.

[2]  O Salonen,et al.  MRI enhancement and microvascular density in gliomas. Correlation with tumor cell proliferation. , 1999, Investigative radiology.

[3]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[4]  P. Carmeliet,et al.  Angiogenesis in cancer and other diseases , 2000, Nature.

[5]  Susan M. Chang,et al.  Glioblastoma Patients Epidermal Growth Factor Receptor , and Survival in Analysis of Complex Relationships between Age , p 53 , Updated , 2001 .

[6]  Z L Gokaslan,et al.  A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. , 2001, Journal of neurosurgery.

[7]  K. Makino,et al.  Influence of p53 mutations on prognosis of patients with glioblastoma , 2002, Cancer.

[8]  A. Padhani,et al.  Reproducibility of dynamic contrast‐enhanced MRI in human muscle and tumours: comparison of quantitative and semi‐quantitative analysis , 2002, NMR in biomedicine.

[9]  Jeffrey L Duerk,et al.  Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI , 2003, Journal of magnetic resonance imaging : JMRI.

[10]  Kenji Tada,et al.  Prognostic value of epidermal growth factor receptor in patients with glioblastoma multiforme. , 2003, Cancer research.

[11]  Glyn Johnson,et al.  Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. , 2004, AJNR. American journal of neuroradiology.

[12]  Raymond Sawaya,et al.  Prognostic significance of preoperative MRI scans in glioblastoma multiforme , 2004, Journal of Neuro-Oncology.

[13]  Ying Lu,et al.  Survival analysis in patients with glioblastoma multiforme: Predictive value of choline‐to‐n‐acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume , 2004, Journal of magnetic resonance imaging : JMRI.

[14]  T W Redpath,et al.  The effects of renal variation upon measurements of perfusion and leakage volume in breast tumours. , 2004, Physics in medicine and biology.

[15]  Paul S Mischel,et al.  MR imaging correlates of survival in patients with high-grade gliomas. , 2005, AJNR. American journal of neuroradiology.

[16]  Koji Yoshimoto,et al.  Molecular determinants of the response of glioblastomas to EGFR kinase inhibitors. , 2005, The New England journal of medicine.

[17]  R. Mirimanoff,et al.  MGMT gene silencing and benefit from temozolomide in glioblastoma. , 2005, The New England journal of medicine.

[18]  R M Weisskoff,et al.  Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. , 2006, AJNR. American journal of neuroradiology.

[19]  P. Kelly,et al.  Perfusion Magnetic Resonance Imaging Predicts Patient Outcome as an Adjunct to Histopathology: A Second Reference Standard in the Surgical and Nonsurgical Treatment of Low-grade Gliomas , 2006, Neurosurgery.

[20]  A. Jackson,et al.  Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? , 2006, AJNR. American journal of neuroradiology.

[21]  Toshihiro Kumabe,et al.  Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. , 2006, Radiology.

[22]  Areen K. Al-Bashir,et al.  New algorithm for quantifying vascular changes in dynamic contrast‐enhanced MRI independent of absolute T1 values , 2007, Magnetic Resonance in Medicine.

[23]  T. Hirai,et al.  Malignant supratentorial astrocytoma treated with postoperative radiation therapy: prognostic value of pretreatment quantitative diffusion-weighted MR imaging. , 2007, Radiology.

[24]  D. Busam,et al.  An Integrated Genomic Analysis of Human Glioblastoma Multiforme , 2008, Science.

[25]  K. Schmainda,et al.  Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. , 2008, Radiology.

[26]  Douglas C. Miller,et al.  Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. , 2008, Radiology.

[27]  G. Johnson,et al.  Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting. , 2010, European journal of radiology.

[28]  J. Debbins,et al.  Optimized Preload Leakage-Correction Methods to Improve the Diagnostic Accuracy of Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging in Posttreatment Gliomas , 2010, American Journal of Neuroradiology.

[29]  Melissa Bondy,et al.  Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 genes involved in the double-strand break repair pathway predict glioblastoma survival. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  H. Mehdorn,et al.  Outcome evaluation in glioblastoma patients using different ranking scores: KPS, GOS, mRS and MRC. , 2010, European journal of cancer care.

[31]  A. Olivi,et al.  Prognostic significance of contrast-enhancing anaplastic astrocytomas in adults. , 2010, Journal of neurosurgery.

[32]  N. Forbes,et al.  Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response , 2010, British Journal of Cancer.

[33]  Konstantin Nikolaou,et al.  Quantitative Pulmonary Perfusion Magnetic Resonance Imaging: Influence of Temporal Resolution and Signal-to-Noise Ratio , 2010, Investigative radiology.

[34]  S. Sourbron.  Technical aspects of MR perfusion. , 2010, European journal of radiology.

[35]  Roger Newson,et al.  Comparing the Predictive Powers of Survival Models Using Harrell's C or Somers’ D , 2010 .

[36]  Erwin G. Van Meir,et al.  Exciting New Advances in Neuro‐Oncology: The Avenue to a Cure for Malignant Glioma , 2010, CA: a cancer journal for clinicians.

[37]  M S Brown,et al.  Apparent Diffusion Coefficient Histogram Analysis Stratifies Progression-Free Survival in Newly Diagnosed Bevacizumab-Treated Glioblastoma , 2011, American Journal of Neuroradiology.

[38]  J. Boxerman,et al.  The Role of Preload and Leakage Correction in Gadolinium-Based Cerebral Blood Volume Estimation Determined by Comparison with MION as a Criterion Standard , 2012, American Journal of Neuroradiology.

[39]  L. Astrakas,et al.  Diffusion tensor and dynamic susceptibility contrast MRI in glioblastoma , 2012, Clinical Neurology and Neurosurgery.

[40]  Rakesh K. Gupta,et al.  Dynamic Contrast-Enhanced Magnetic Resonance Imaging–Derived kep as a Potential Biomarker of Matrix Metalloproteinase 9 Expression in Patients With Glioblastoma Multiforme: A Pilot Study , 2012, Journal of computer assisted tomography.

[41]  Young Suk Kim,et al.  MGMT gene promoter methylation as a potent prognostic factor in glioblastoma treated with temozolomide-based chemoradiotherapy: a single-institution study. , 2012, International journal of radiation oncology, biology, physics.

[42]  T. Jiang,et al.  Glioblastoma with an oligodendroglioma component: distinct clinical behavior, genetic alterations, and outcome. , 2012, Neuro-oncology.

[43]  M. Manzoni,et al.  Gastroenteric neuroendocrine neoplasms classification: Comparison of prognostic models , 2013, Cancer.

[44]  Michael Ingrisch,et al.  Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer , 2013, Journal of Pharmacokinetics and Pharmacodynamics.

[45]  Song-Cheol Kim,et al.  Increased number of metastatic lymph nodes in adenocarcinoma of the ampulla of Vater as a prognostic factor: a proposal of new nodal classification. , 2014, Surgery.

[46]  Seong Ho Park,et al.  Glioma: Application of Histogram Analysis of Pharmacokinetic Parameters from T1-Weighted Dynamic Contrast-Enhanced MR Imaging to Tumor Grading , 2014, American Journal of Neuroradiology.

[47]  H. Goldschmidt,et al.  Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  S. Choi,et al.  Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. , 2015, Radiology.

[49]  R. Thornhill,et al.  Preoperative Prognostic Value of Dynamic Contrast-Enhanced MRI–Derived Contrast Transfer Coefficient and Plasma Volume in Patients with Cerebral Gliomas , 2015, American Journal of Neuroradiology.

[50]  David Bonekamp,et al.  Association of overall survival in patients with newly diagnosed glioblastoma with contrast‐enhanced perfusion MRI: Comparison of intraindividually matched T1‐ and T2*‐based bolus techniques , 2015, Journal of magnetic resonance imaging : JMRI.

[51]  À. Rovira,et al.  Pixel-by-Pixel Comparison of Volume Transfer Constant and Estimates of Cerebral Blood Volume from Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast-Enhanced MR Imaging in High-Grade Gliomas , 2015, American Journal of Neuroradiology.