MRI Atlas of IDH Wild-Type Supratentorial Glioblastoma: Probabilistic Maps of Phenotype, Management, and Outcomes.

Background Tumor location is a main prognostic parameter in patients with glioblastoma. Probabilistic MRI-based brain atlases specifying the probability of tumor location associated with important demographic, clinical, histomolecular, and management data are lacking for isocitrate dehydrogenase (IDH) wild-type glioblastomas. Purpose To correlate glioblastoma location with clinical phenotype, surgical management, and outcomes by using a probabilistic analysis in a three-dimensional (3D) MRI-based atlas. Materials and Methods This retrospective study included all adults surgically treated for newly diagnosed IDH wild-type supratentorial glioblastoma in a tertiary adult surgical neuro-oncology center (2006-2016). Semiautomated tumor segmentation and spatial normalization procedures to build a 3D MRI-based atlas were validated. The authors performed probabilistic analyses by using voxel-based lesion symptom mapping technology. The Liebermeister test was used for binary data, and the generalized linear model was used for continuous data. Results A total of 392 patients (mean age, 61 years ± 13; 233 men) were evaluated. The authors identified the preferential location of glioblastomas according to subventricular zone, age, sex, clinical presentation, revised Radiation Therapy Oncology Group-Recursive Partitioning Analysis class, Karnofsky performance status, O6-methylguanine DNA methyltransferase promoter methylation status, surgical management, and survival. The superficial location distant from the eloquent area was more likely associated with a preserved functional status at diagnosis (348 of 392 patients [89%], P < .05), a large surgical resection (173 of 392 patients [44%], P < .05), and prolonged overall survival (163 of 334 patients [49%], P < .05). In contrast, deep location and location within eloquent brain areas were more likely associated with an impaired functional status at diagnosis (44 of 392 patients [11%], P < .05), a neurologic deficit (282 of 392 patients [72%], P < .05), treatment with biopsy only (183 of 392 patients [47%], P < .05), and shortened overall survival (171 of 334 patients [51%], P < .05). Conclusion The authors identified the preferential location of isocitrate dehydrogenase wild-type glioblastomas according to parameters of interest and provided an image-based integration of multimodal information impacting survival results. This suggests the role of glioblastoma location as a surrogate and multimodal parameter integrating several known prognostic factors. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Huang in this issue.

[1]  Henry Brem,et al.  Establishing percent resection and residual volume thresholds affecting survival and recurrence for patients with newly diagnosed intracranial glioblastoma. , 2014, Neuro-oncology.

[2]  Josep Marco-Pallarés,et al.  Analysis of automated methods for spatial normalization of lesioned brains , 2012, NeuroImage.

[3]  D. Nelson,et al.  Influence of location and extent of surgical resection on survival of patients with glioblastoma multiforme: results of three consecutive Radiation Therapy Oncology Group (RTOG) clinical trials. , 1993, International journal of radiation oncology, biology, physics.

[4]  David T. W. Jones,et al.  Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma , 2012, Nature.

[5]  H. Rolf Jäger,et al.  Enantiomorphic normalization of focally lesioned brains , 2008, NeuroImage.

[6]  Marie Blonski,et al.  A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain , 2016, PloS one.

[7]  Martin Sill,et al.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. , 2016, Radiology.

[8]  Amar Gajjar,et al.  The genomic landscape of diffuse intrinsic pontine glioma and pediatric non-brainstem high-grade glioma , 2014, Nature Genetics.

[9]  W. Poon,et al.  A Comparative Analysis of the Usefulness of Survival Prediction Models for Patients with Glioblastoma in the Temozolomide Era: The Importance of Methylguanine Methyltransferase Promoter Methylation, Extent of Resection, and Subventricular Zone Location. , 2018, World neurosurgery.

[10]  Aeilko H. Zwinderman,et al.  Resection Probability Maps for Quality Assessment of Glioma Surgery without Brain Location Bias , 2013, PloS one.

[11]  Lei Wang,et al.  Age-associated brain regions in gliomas: a volumetric analysis , 2015, Journal of Neuro-Oncology.

[12]  Karl J. Friston,et al.  Spatial normalization of lesioned brains: Performance evaluation and impact on fMRI analyses , 2007, NeuroImage.

[13]  R. Spetzler,et al.  Impact of removed tumor volume and location on patient outcome in glioblastoma , 2017, Journal of Neuro-Oncology.

[14]  Christos Davatzikos,et al.  Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma , 2016, NeuroImage: Clinical.

[15]  Ron Kikinis,et al.  Morphological characteristics of brain tumors causing seizures. , 2010, Archives of neurology.

[16]  Hang-gen Du,et al.  Correlation Between Tumor Location and Clinical Properties of Glioblastomas in Frontal and Temporal Lobes. , 2018, World neurosurgery.

[17]  Mitchel S. Berger,et al.  Unique astrocyte ribbon in adult human brain contains neural stem cells but lacks chain migration , 2004, Nature.

[18]  Robert J. Harris,et al.  Anatomic localization of O6-methylguanine DNA methyltransferase (MGMT) promoter methylated and unmethylated tumors: A radiographic study in 358 de novo human glioblastomas , 2012, NeuroImage.

[19]  Qiang Tian,et al.  Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis , 2018, BMC Cancer.

[20]  N. Warrington,et al.  Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data , 2019, Science Translational Medicine.

[21]  M. Zanello,et al.  Relationship between tumour location and preoperative seizure incidence depends on glioma grade of malignancy. , 2016, Epileptic disorders : international epilepsy journal with videotape.

[22]  Chris Rorden,et al.  Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking , 2001, NeuroImage.

[23]  A. Kaye,et al.  Frontal glioblastoma multiforme may be biologically distinct from non-frontal and multilobar tumors , 2016, Journal of Clinical Neuroscience.

[24]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.