Advanced magnetic resonance imaging in glioblastoma: a review.

Glioblastoma, the most common and most rapidly progressing primary malignant tumor of the central nervous system, continues to portend a dismal prognosis, despite improvements in diagnostic and therapeutic strategies over the last 20 years. The standard of care radiographic characterization of glioblastoma is magnetic resonance imaging (MRI), which is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma. Basic MRI modalities available from any clinical scanner, including native T1-weighted (T1w) and contrast-enhanced (T1CE), T2-weighted (T2w), and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences, provide critical clinical information about various processes in the tumor environment. In the last decade, advanced MRI modalities are increasingly utilized to further characterize glioblastomas more comprehensively. These include multi-parametric MRI sequences, such as dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE), higher order diffusion techniques such as diffusion tensor imaging (DTI), and MR spectroscopy (MRS). Significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. Functional MRI (fMRI) and tractography are increasingly being used to identify eloquent cortices and important tracts to minimize postsurgical neuro-deficits. A contemporary review of the application of standard and advanced MRI in clinical neuro-oncologic practice is presented here.

[1]  Christos Davatzikos,et al.  In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ϕ-Index , 2017, Clinical Cancer Research.

[2]  Evaluation of the Lactate-toN-Acetyl-aspartate Ratio Defined With Magnetic Resonance Spectroscopic Imaging Before Radiation Therapy as a New Predictive Marker of the Site of Relapse in Patients With Glioblastoma Multiforme , 2014 .

[3]  S. Cha,et al.  Update on brain tumor imaging: from anatomy to physiology. , 2006, AJNR. American journal of neuroradiology.

[4]  B. Małkowski,et al.  Pre-irradiation tumour volumes defined by MRI and dual time-point FET-PET for the prediction of glioblastoma multiforme recurrence: A prospective study. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[5]  P. Bagri,et al.  Addition of magnetic resonance imaging to computed tomography-based three-dimensional conformal radiotherapy planning for postoperative treatment of astrocytomas: Changes in tumor volume and isocenter shift , 2015, South Asian Journal of Cancer.

[6]  Walter J Curran,et al.  Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  Mitchel S Berger,et al.  Functional outcome after language mapping for glioma resection. , 2008, The New England journal of medicine.

[8]  A. Field,et al.  Mean apparent diffusion coefficient values in defining radiotherapy planning target volumes in glioblastoma. , 2015, Quantitative imaging in medicine and surgery.

[9]  Thomas Filleron,et al.  Evaluation of the lactate-to-N-acetyl-aspartate ratio defined with magnetic resonance spectroscopic imaging before radiation therapy as a new predictive marker of the site of relapse in patients with glioblastoma multiforme. , 2014, International journal of radiation oncology, biology, physics.

[10]  R. Kerbel Tumor angiogenesis: past, present and the near future. , 2000, Carcinogenesis.

[11]  Gang Wang,et al.  Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors , 2016, Radiation Oncology.

[12]  A. Kaye,et al.  Early perfusion MRI predicts survival outcome in patients with recurrent glioblastoma treated with bevacizumab and carboplatin , 2016, Journal of Neuro-Oncology.

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

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

[15]  Martin Sill,et al.  Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response , 2016, Clinical Cancer Research.

[16]  Susan M. Chang,et al.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Luke Macyszyn,et al.  Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. , 2016, Neuro-oncology.

[18]  P. Wen,et al.  Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma , 2016, Journal of Neuro-Oncology.

[19]  Christos Davatzikos,et al.  Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework , 2016, BrainLes@MICCAI.

[20]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[21]  Max Wintermark,et al.  Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. , 2015, Neuro-oncology.

[22]  Maciej A Mazurowski,et al.  Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study , 2017, Journal of Neuro-Oncology.

[23]  J. Barnholtz-Sloan,et al.  American Brain Tumor Association Adolescent and Young Adult Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012. , 2016, Neuro-oncology.

[24]  J. Chaganti,et al.  Accuracy of percentage of signal intensity recovery and relative cerebral blood volume derived from dynamic susceptibility-weighted, contrast-enhanced MRI in the preoperative diagnosis of cerebral tumours , 2015, The neuroradiology journal.

[25]  K Sartor,et al.  Early postoperative magnetic resonance imaging after resection of malignant glioma: objective evaluation of residual tumor and its influence on regrowth and prognosis. , 1994, Neurosurgery.

[26]  P. LaViolette,et al.  Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme , 2015, Journal of Neuro-Oncology.

[27]  R. Velthuizen,et al.  Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. , 2004, International journal of radiation oncology, biology, physics.

[28]  Jae-Uk Jeong,et al.  Evaluation of variability in target volume delineation for newly diagnosed glioblastoma: a multi-institutional study from the Korean Radiation Oncology Group , 2015, Radiation Oncology.

[29]  Z. Ram,et al.  Dynamics of FLAIR Volume Changes in Glioblastoma and Prediction of Survival , 2017, Annals of Surgical Oncology.

[30]  T. Cloughesy,et al.  Response Assessment Criteria for Glioblastoma: Practical Adaptation and Implementation in Clinical Trials of Antiangiogenic Therapy , 2013, Current Neurology and Neuroscience Reports.

[31]  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.

[32]  Bilwaj Gaonkar,et al.  GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation , 2015, Brainles@MICCAI.

[33]  Kevin Petrecca,et al.  Failure pattern following complete resection plus radiotherapy and temozolomide is at the resection margin in patients with glioblastoma , 2012, Journal of Neuro-Oncology.

[34]  Albert Lai,et al.  Time course of imaging changes of GBM during extended bevacizumab treatment , 2008, Journal of Neuro-Oncology.

[35]  Dima Suki,et al.  Association of the Extent of Resection With Survival in Glioblastoma: A Systematic Review and Meta-analysis. , 2016, JAMA oncology.

[36]  Eduard Schreibmann,et al.  Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients. , 2016, Neuro-oncology.

[37]  T. Cloughesy,et al.  MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy , 2006, Neurology.

[38]  John A Butman,et al.  Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Paolo G P Nucifora,et al.  Use of diffusion tensor imaging in glioma resection. , 2013, Neurosurgical focus.

[40]  Mitchel S. Berger,et al.  Operative techniques for gliomas and the value of extent of resection , 2009, Neurotherapeutics.

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

[42]  H. Aerts The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. , 2016, JAMA oncology.

[43]  T. Cascino,et al.  Response criteria for phase II studies of supratentorial malignant glioma. , 1990, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[44]  Shiao Y. Woo,et al.  Evaluation of peritumoral edema in the delineation of radiotherapy clinical target volumes for glioblastoma. , 2007, International journal of radiation oncology, biology, physics.

[45]  Seung-Koo Lee,et al.  Diffusion tensor and perfusion imaging of brain tumors in high-field MR imaging. , 2012, Neuroimaging clinics of North America.

[46]  J. K. Smith,et al.  Vessel tortuosity and brain tumor malignancy: a blinded study. , 2005, Academic radiology.

[47]  J. Lemée,et al.  Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone. , 2015, Neuro-oncology.

[48]  G. Biros,et al.  Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma. , 2016, Neurosurgery.

[49]  Tao Jiang,et al.  Residual low ADC and high FA at the resection margin correlate with poor chemoradiation response and overall survival in high-grade glioma patients. , 2016, European journal of radiology.

[50]  Luke Macyszyn,et al.  Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. , 2014, Radiology.

[51]  J. Gillard,et al.  Imaging biomarkers of brain tumour margin and tumour invasion. , 2011, The British journal of radiology.

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

[53]  Tae Min Kim,et al.  Prognosis prediction of non-enhancing T2 high signal intensity lesions in glioblastoma patients after standard treatment: application of dynamic contrast-enhanced MR imaging , 2017, European Radiology.

[54]  A. Brandes,et al.  MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[55]  Geoffrey S Young,et al.  Advanced MRI of adult brain tumors. , 2007, Neurologic clinics.

[56]  Estanislao Arana,et al.  Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study , 2017, European Radiology.

[57]  Naoya Hashimoto,et al.  Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. , 2015, The Lancet. Oncology.

[58]  M A Deeley,et al.  Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study , 2011, Physics in medicine and biology.