Local Fractional Anisotropy Is Reduced in Areas with Tumor Recurrence in Glioblastoma.

Purpose To analyze if fractional anisotropy (FA) in nonenhancing peritumoral regions (NEPTRs) at baseline is associated with later tumor recurrence in glioblastoma. Materials and Methods Ethical approval was obtained for this retrospective, HIPAA-compliant study. FA was measured in 70 patients with glioblastoma in five regions of interest (ROIs) per patient in the NEPTR at preoperative magnetic resonance (MR) imaging with (166 regions) or without (184 regions) local contrast-enhancing tumor recurrence at follow-up MR imaging (median, 7.3 months; range, 0.9-46.6 months). ROIs were classified according to their location (white matter, cortex, fiber tracts, basal ganglia). Ratio of FA in the ROI of the NEPTR to that in the contralateral side (FAcontra) and to that in the internal capsule (FAint) was calculated. A generalized linear mixed model was performed. Ten-fold cross-validation was used for the receiver operating characteristics (ROC) analysis. Results FAcontra and FAint were significantly lower in regions with later tumor recurrence than in regions without (median FAcontra: 0.29 [interquartile range {IR}, 0.22-0.36] vs 0.46 [IR, 0.38-0.57]; median FAint: 0.20 [IR, 0.16-0.24] vs 0.29 [IR, 0.22-0.36], respectively). ROC analysis revealed an area under the ROC curve of 0.893 for FAcontra and of 0.815 for FAint, resulting in respective sensitivity and specificity of 85.5% and 84.2% for FAcontra and 86.7% and 66.8% for FAint. Conclusion Local tumor recurrence in the NEPTR may be predicted by FA metrics at baseline in patients with glioblastoma. This might be important for surgery or radiation planning. © RSNA, 2016 Online supplemental material is available for this article.

[1]  B. Yin,et al.  Can Diffusion Tensor Imaging Noninvasively Detect IDH1 Gene Mutations in Astrogliomas? A Retrospective Study of 112 Cases , 2014, American Journal of Neuroradiology.

[2]  T. Carpenter,et al.  Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. , 2006, AJNR. American journal of neuroradiology.

[3]  J D Pickard,et al.  Diffusion tensor imaging: possible implications for radiotherapy treatment planning of patients with high-grade glioma. , 2005, Clinical oncology (Royal College of Radiologists (Great Britain)).

[4]  E. Hoving,et al.  The role of diffusion tensor imaging in brain tumor surgery: A review of the literature , 2014, Clinical Neurology and Neurosurgery.

[5]  Peter McGraw,et al.  Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. , 2004, Radiology.

[6]  K. Swanson,et al.  A mathematical model for brain tumor response to radiation therapy , 2009, Journal of mathematical biology.

[7]  Kevin A Hallgren,et al.  Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. , 2012, Tutorials in quantitative methods for psychology.

[8]  Jan J. Heimans,et al.  Early postoperative MRI overestimates residual tumour after resection of gliomas with no or minimal enhancement , 2011, European Radiology.

[9]  Juha Öhman,et al.  Repeatability and variation of region-of-interest methods using quantitative diffusion tensor MR imaging of the brain , 2012, BMC Medical Imaging.

[10]  Glyn Johnson,et al.  High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. , 2002, Radiology.

[11]  L Junck,et al.  Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. , 2011, The Lancet. Oncology.

[12]  T. Watabe,et al.  A Novel PET Index, 18F-FDG–11C-Methionine Uptake Decoupling Score, Reflects Glioma Cell Infiltration , 2012, The Journal of Nuclear Medicine.

[13]  Matthew S. Johnson,et al.  Responsible conduct of radiology research. Part V. The Health Insurance Portability and Accountability Act and research. , 2005, Radiology.

[14]  M. Fujiki,et al.  Comparison of Multiple Parameters Obtained on 3T Pulsed Arterial Spin-Labeling, Diffusion Tensor Imaging, and MRS and the Ki-67 Labeling Index in Evaluating Glioma Grading , 2014, American Journal of Neuroradiology.

[15]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

[16]  M. Weller,et al.  Biological tumor volume in 18FET-PET before radiochemotherapy correlates with survival in GBM , 2015, Neurology.

[17]  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.

[18]  M. Berger,et al.  Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence. , 2002, Journal of neurosurgery.

[19]  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.

[20]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[21]  Christopher Nimsky,et al.  Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. , 2006, Radiology.

[22]  G. Johnson,et al.  Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. , 2003, AJNR. American journal of neuroradiology.

[23]  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.

[24]  Amy S Nowacki,et al.  Residual tumor volume versus extent of resection: predictors of survival after surgery for glioblastoma. , 2014, Journal of neurosurgery.

[25]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[26]  Albert Lai,et al.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. , 2009, Cancer research.

[27]  Tarik Tihan,et al.  Volumetric extent of resection and residual contrast enhancement on initial surgery as predictors of outcome in adult patients with hemispheric anaplastic astrocytoma. , 2006, Journal of neurosurgery.

[28]  C. Zimmer,et al.  Postoperative ischemic changes following resection of newly diagnosed and recurrent gliomas and their clinical relevance. , 2013, Journal of neurosurgery.

[29]  P. Dechent,et al.  Abnormalities in the normal appearing white matter of the cerebral hemisphere contralateral to a malignant brain tumor detected by diffusion tensor imaging. , 2014, Folia neuropathologica.

[30]  Frederik Barkhof,et al.  Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map , 2014, Journal of Neuro-Oncology.

[31]  E. Melhem,et al.  Differentiation between Glioblastomas, Solitary Brain Metastases, and Primary Cerebral Lymphomas Using Diffusion Tensor and Dynamic Susceptibility Contrast-Enhanced MR Imaging , 2011, American Journal of Neuroradiology.

[32]  P. Desmond,et al.  Diffusion Tensor Imaging in Glioblastoma Multiforme and Brain Metastases: The Role of p, q, L, and Fractional Anisotropy , 2008, American Journal of Neuroradiology.

[33]  Sungheon Kim,et al.  Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging , 2009, NeuroImage.

[34]  T. Goto,et al.  Diffusion tensor-based tumor infiltration index cannot discriminate vasogenic edema from tumor-infiltrated edema , 2010, Journal of Neuro-Oncology.

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

[36]  Michael Weller,et al.  Standards of care for treatment of recurrent glioblastoma--are we there yet? , 2013, Neuro-oncology.

[37]  James T. Patrie,et al.  Multimodal MR imaging model to predict tumor infiltration in patients with gliomas , 2014, Neuroradiology.

[38]  Yukihiko Fujii,et al.  Diffusion tensor analysis of peritumoral edema using lambda chart analysis indicative of the heterogeneity of the microstructure within edema. , 2005, Journal of neurosurgery.

[39]  Zhe Zhang,et al.  Lower apparent diffusion coefficients indicate distinct prognosis in low-grade and high-grade glioma , 2014, Journal of Neuro-Oncology.

[40]  A. Server,et al.  Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis , 2011, Neuroradiology.

[41]  A. Server,et al.  Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. , 2014, European journal of radiology.

[42]  Ming Zhang,et al.  Differentiation of pure vasogenic edema and tumor-infiltrated edema in patients with peritumoral edema by analyzing the relationship of axial and radial diffusivities on 3.0T MRI , 2013, Clinical Neurology and Neurosurgery.

[43]  Glyn Johnson,et al.  Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. , 2004, Radiology.

[44]  Timothy J. Herron,et al.  Regional variation, hemispheric asymmetries and gender differences in pericortical white matter , 2011, NeuroImage.

[45]  Tommaso Scarabino,et al.  Multiparametric 3T MR approach to the assessment of cerebral gliomas: tumor extent and malignancy , 2006, Neuroradiology.

[46]  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.

[47]  Alexander R A Anderson,et al.  Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: in Silico Modeling Integrates Imaging and Histology Nih Public Access Author Manuscript Introduction , 2011 .