Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival.

BACKGROUND Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. METHODS We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. RESULTS Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively). CONCLUSIONS Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

[1]  M. Berger,et al.  Corpus callosum involvement as a prognostic factor for patients with high-grade astrocytoma. , 1997, International journal of radiation oncology, biology, physics.

[2]  Olivier Gevaert,et al.  Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. , 2015, Journal of neuroradiology. Journal de neuroradiologie.

[3]  Susan M. Chang,et al.  A critical evaluation of cystic features in primary glioblastoma as a prognostic factor for survival. , 2011, Journal of neurosurgery.

[4]  Eduard Schreibmann,et al.  Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials. , 2014, Translational oncology.

[5]  R. McLendon,et al.  IDH1 and IDH2 mutations in gliomas. , 2009, The New England journal of medicine.

[6]  William D. Dunn,et al.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. , 2013, Radiology.

[7]  F. Gibberd,et al.  Epilepsy and other factors in the prognosis of gliomas , 1980, Acta neurologica Scandinavica.

[8]  Tracy T Batchelor,et al.  Magnetic Resonance Imaging Characteristics Predict Epidermal Growth Factor Receptor Amplification Status in Glioblastoma , 2005, Clinical Cancer Research.

[9]  M. Faist,et al.  The role of tumor resection in the treatment of glioblastoma multiforme in adults , 1999, Cancer.

[10]  N. Hata,et al.  An integrated visualization system for surgical planning and guidance using image fusion and an open MR , 2001, Journal of magnetic resonance imaging : JMRI.

[11]  M. D. de Leon,et al.  CT prognostic criteria of survival after malignant glioma surgery. , 1983, AJNR. American journal of neuroradiology.

[12]  R. Lober,et al.  The incidence and significance of multiple lesions in glioblastoma , 2013, Journal of Neuro-Oncology.

[13]  B. Tucer,et al.  Analysis of the mortality probability of preoperative MRI features in malignant astrocytomas. , 2011, Turkish neurosurgery.

[14]  Jordan M. Malof,et al.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients. , 2013, Neuro-oncology.

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

[16]  David A Gutman,et al.  Glioblastoma: imaging genomic mapping reveals sex-specific oncogenic associations of cell death. , 2015, Radiology.

[17]  A. Olivi,et al.  Surgical outcomes for older patients with glioblastoma multiforme: preoperative factors associated with decreased survival. Clinical article. , 2011, Journal of neurosurgery.

[18]  K. Takakura,et al.  Quantitative imaging study of extent of surgical resection and prognosis of malignant astrocytomas. , 1997, Neurosurgery.

[19]  S B Green,et al.  Patient age, histologic features, and length of survival in patients with glioblastoma multiforme , 1987, Cancer.

[20]  Veit Rohde,et al.  EXTENT OF RESECTION AND SURVIVAL IN GLIOBLASTOMA MULTIFORME: IDENTIFICATION OF AND ADJUSTMENT FOR BIAS , 2008, Neurosurgery.

[21]  E. Gehan,et al.  Prognostic factors for patients with brain tumors. , 1977, National Cancer Institute monograph.

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

[23]  David Gutman,et al.  Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. , 2013, Radiology.

[24]  Rivka R. Colen,et al.  Statistical feature selection for enhanced detection of brain tumor , 2014, Optics & Photonics - Optical Engineering + Applications.

[25]  Keith Paulsen,et al.  Genetics of glioblastoma: a window into its imaging and histopathologic variability. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[26]  Ahmad Chaddad,et al.  Quantitative texture analysis for Glioblastoma phenotypes discrimination , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[27]  G. Ojemann,et al.  Imaging features of invasion and preoperative and postoperative tumor burden in previously untreated glioblastoma: Correlation with survival , 2010, Surgical neurology international.

[28]  A. Verma.  MGMT Gene Silencing and Benefit From Temozolomide in Glioblastoma , 2006 .

[29]  J. Uhm.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

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

[31]  Ferenc A. Jolesz,et al.  Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.

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

[33]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

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

[35]  D. Gilbert,et al.  Glioblastoma multiforme is not a uniform disease! , 1981, Cancer clinical trials.

[36]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[37]  L. Miller,et al.  Comparison of clinical outcomes and genomic characteristics of single focus and multifocal glioblastoma , 2014, Journal of Neuro-Oncology.

[38]  Ferenc A Jolesz,et al.  A validation framework for brain tumor segmentation. , 2007, Academic radiology.

[39]  George Fountzilas,et al.  Volumetric and MGMT parameters in glioblastoma patients: Survival analysis , 2012, BMC Cancer.

[40]  Ron Kikinis,et al.  A statistically based flow for image segmentation , 2004, Medical Image Anal..

[41]  Kenneth R. Hess,et al.  Extent of Resection as a Prognostic Variable in the Treatment of Gliomas , 1999, Journal of Neuro-Oncology.

[42]  Ahmad Chaddad,et al.  Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier , 2014, 2014 48th Annual Conference on Information Sciences and Systems (CISS).