Prognosis prediction of non-enhancing T2 high signal intensity lesions in glioblastoma patients after standard treatment: application of dynamic contrast-enhanced MR imaging

AbstractObjectivesTo identify candidate imaging biomarkers for early disease progression in glioblastoma multiforme (GBM) patients by analysis of dynamic contrast-enhanced (DCE) MR parameters of non-enhancing T2 high signal intensity (SI) lesions.MethodsForty-nine GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. According to the Response Assessment in Neuro-Oncology criteria, patients were classified into progression (n = 21) or non-progression (n = 28) groups. We analysed the pharmacokinetic parameters of Ktrans, Ve and Vp within non-enhancing T2 high SI lesions of each tumour. The best percentiles of each parameter from cumulative histograms were identified by the area under the receiver operating characteristic curve (AUC) and were compared using multivariate stepwise logistic regression.ResultsFor the differentiation of early disease progression, the highest AUC values were found in the 99th percentile of Ktrans (AUC 0.954), the 97th percentile of Ve (AUC 0.815) and the 94th percentile of Vp (AUC 0.786) (all p < 0.05). The 99th percentile of Ktrans was the only significant independent variable from the multivariate stepwise logistic regression (p = 0.002).ConclusionsWe found that the Ktrans of non-enhancing T2 high SI lesions in GBM patients holds potential as a candidate prognostic marker in future prospective studies.Key Points• DCE MR imaging provides candidate prognostic marker of GBM after standard treatment. • Cumulative histogram was applied to include entire non-enhancing T2 high SI lesions. • The 99th percentile value of Ktrans was the most likely potential biomarker.

[1]  Rajan Jain,et al.  Measurements of tumor vascular leakiness using DCE in brain tumors: clinical applications , 2013, NMR in biomedicine.

[2]  P S Tofts,et al.  Quantitative Analysis of Dynamic Gd‐DTPA Enhancement in Breast Tumors Using a Permeability Model , 1995, Magnetic resonance in medicine.

[3]  Alan Jackson,et al.  Comparative study of methods for determining vascular permeability and blood volume in human gliomas , 2004, Journal of magnetic resonance imaging : JMRI.

[4]  M. J. van den Bent,et al.  Pseudoprogression and pseudoresponse in the treatment of gliomas , 2009, Current opinion in neurology.

[5]  D. Geng,et al.  Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging , 2012, Journal of Clinical Neuroscience.

[6]  Paul S Tofts,et al.  Apparent diffusion coefficient histograms may predict low‐grade glioma subtype , 2007, NMR in biomedicine.

[7]  Mitchel S Berger,et al.  Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. , 2012, Neuro-oncology.

[8]  M. Idoate,et al.  Pathological characterization of the glioblastoma border as shown during surgery using 5‐aminolevulinic acid‐induced fluorescence , 2011, Neuropathology : official journal of the Japanese Society of Neuropathology.

[9]  N. Magné,et al.  Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation , 2016, European Radiology.

[10]  B. Fisher,et al.  Supratentorial malignant glioma: patterns of recurrence and implications for external beam local treatment. , 1992, International journal of radiation oncology, biology, physics.

[11]  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.

[12]  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.

[13]  Jinna Kim,et al.  Glioma Grading Capability: Comparisons among Parameters from Dynamic Contrast-Enhanced MRI and ADC Value on DWI , 2013, Korean journal of radiology.

[14]  T. Chenevert,et al.  The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. , 2006, Cancer research.

[15]  R. Mirimanoff,et al.  Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. , 2009, The Lancet. Oncology.

[16]  A. Falini,et al.  Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps , 2016, European Radiology.

[17]  Dinggang Shen,et al.  Robust Computation of Mutual Information Using Spatially Adaptive Meshes , 2007, MICCAI.

[18]  Martin J. van den Bent,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[19]  P. Tofts,et al.  Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.

[20]  F. Zanella,et al.  Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. , 2006, The Lancet. Oncology.

[21]  Zhi-Xiong Lin,et al.  Peritumoral edema shown by MRI predicts poor clinical outcome in glioblastoma , 2015, World Journal of Surgical Oncology.

[22]  R. Thornhill,et al.  Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas , 2015, American Journal of Neuroradiology.

[23]  Chul-Kee Park,et al.  Cerebral Blood Volume Analysis in Glioblastomas Using Dynamic Susceptibility Contrast-Enhanced Perfusion MRI: A Comparison of Manual and Semiautomatic Segmentation Methods , 2013, PloS one.

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

[25]  Ho Sung Kim,et al.  Apparent diffusion coefficient parametric response mapping MRI for follow-up of glioblastoma , 2016, European Radiology.

[26]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[27]  Kjell Johnson,et al.  Evaluating Methods for Classifying Expression Data , 2004, Journal of biopharmaceutical statistics.

[28]  P. Wen,et al.  An exploratory survival analysis of anti-angiogenic therapy for recurrent malignant glioma , 2009, Journal of Neuro-Oncology.

[29]  J. Martinez-Climent,et al.  Cellular Plasticity Confers Migratory and Invasive Advantages to a Population of Glioblastoma‐Initiating Cells that Infiltrate Peritumoral Tissue , 2013, Stem cells.

[30]  Scott N. Hwang,et al.  Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. , 2014, Radiology.

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

[32]  H. Heinzl,et al.  Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma? , 2009, European journal of neurology.

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

[34]  G. Maira,et al.  Invasive tumor cells and prognosis in a selected population of patients with glioblastoma multiforme , 2008, Cancer.

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

[36]  M. Aref,et al.  Comparison of tumor histology to dynamic contrast enhanced magnetic resonance imaging-based physiological estimates. , 2008, Magnetic resonance imaging.

[37]  Rakesh K. Gupta,et al.  Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI , 2008, Neuroradiology.

[38]  W W Hauck,et al.  A proposal for examining and reporting stepwise regressions. , 1991, Statistics in medicine.

[39]  J. Uhm Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group , 2010 .