In vivo assessment of tumor heterogeneity in WHO 2016 glioma grades using diffusion kurtosis imaging: Diagnostic performance and improvement of feasibility in routine clinical practice.

PURPOSE To assess the diagnostic performance of normalized and non-normalized diffusion kurtosis imaging (DKI) metrics extracted from different tumor volume data for grading glioma according to the integrated approach of the revised 2016 WHO classification. MATERIALS AND METHODS Sixty patients with histopathologically confirmed glioma, who provided written informed consent, were retrospectively assessed between 01/2013 and 08/2016 from a prospective trial approved by the local institutional review board. Mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were assessed by two blinded physicians from four different volumes of interest (VOI): whole solid tumor including (VOItu-ed) and excluding perifocal edema (VOItu), infiltrative zone (VOIed), and single slice of solid tumor core (VOIslice). Intra-class correlation coefficient (ICC) was calculated to assess inter-rater agreement. One-way ANOVA was used to compare MK between 2016 CNS WHO tumor grades. Friedman's test compared MK and MD of each VOI. Spearman's correlation coefficient was used to correlate MK with 2016 CNS WHO tumor grades. ROC analysis was performed on MK for significant results. RESULTS The MK assessment showed excellent inter-rater agreement for each VOI (ICC, 0.906-0.955). MK was significantly lower in IDHmutant astrocytoma (0.40±0.07), than in 1p/19q-confirmed oligodendroglioma (0.54±0.10, P=0.001) or IDHwild-type glioblastoma (0.68±0.13, P<0.001). MK and 2016 WHO tumor grades were strongly and positively correlated (VOItu-ed, r=0.684; VOItu, r=0.734; VOIed, r=0.625; VOIslice, r=0.698; P<0.001). CONCLUSIONS Non-normalized MK values obtained from VOItu and VOIslice showed the best reproducibility and highest diagnostic performance for stratifying glioma according to the integrated approach of the recent 2016 WHO classification.

[1]  Seung Hong Choi,et al.  Gliomas: Histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade. , 2011, Radiology.

[2]  Volker Hovestadt,et al.  Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities , 2015, Acta Neuropathologica.

[3]  Deric M. Park,et al.  The Evidence of Glioblastoma Heterogeneity , 2015, Scientific Reports.

[4]  Se Hoon Kim,et al.  Clinical, histological, and immunohistochemical features predicting 1p/19q loss of heterozygosity in oligodendroglial tumors , 2005, Acta Neuropathologica.

[5]  J. Qin,et al.  Comparison of the values of MRI diffusion kurtosis imaging and diffusion tensor imaging in cerebral astrocytoma grading and their association with aquaporin-4. , 2016, Neurology India.

[6]  Adriana Di Martino,et al.  Age‐related non‐Gaussian diffusion patterns in the prefrontal brain , 2008, Journal of magnetic resonance imaging : JMRI.

[7]  S. Al-Sarraj,et al.  IDH1-Associated Primary Glioblastoma in Young Adults Displays Differential Patterns of Tumour and Vascular Morphology , 2013, PloS one.

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

[9]  J. Helpern,et al.  Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging , 2006, NMR in biomedicine.

[10]  R. Sevick,et al.  How often are nonenhancing supratentorial gliomas malignant? A population study , 2002, Neurology.

[11]  H. Zentgraf,et al.  Characterization of R132H Mutation‐specific IDH1 Antibody Binding in Brain Tumors , 2010, Brain pathology.

[12]  S. Suo,et al.  Apparent diffusion coefficient measurement in glioma: Influence of region‐of‐interest determination methods on apparent diffusion coefficient values, interobserver variability, time efficiency, and diagnostic ability , 2017, Journal of magnetic resonance imaging : JMRI.

[13]  D. Kondziolka,et al.  Mutant IDH1 and thrombosis in gliomas , 2016, Acta Neuropathologica.

[14]  David H. Salat,et al.  Non-Gaussian water diffusion in aging white matter , 2014, Neurobiology of Aging.

[15]  H. Lanfermann,et al.  Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. , 2010, Radiology.

[16]  Salvador Castaneda Vega,et al.  In vivo molecular profiling of human glioma using diffusion kurtosis imaging , 2016, Journal of Neuro-Oncology.

[17]  Jingzhen He,et al.  Evaluation of histopathological changes in the microstructure at the center and periphery of glioma tumors using diffusional kurtosis imaging , 2016, Clinical Neurology and Neurosurgery.

[18]  Christian Mawrin,et al.  Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas , 2009, Acta Neuropathologica.

[19]  Jan Sijbers,et al.  Gliomas: diffusion kurtosis MR imaging in grading. , 2012, Radiology.

[20]  David T. W. Jones,et al.  ATRX and IDH1-R132H immunohistochemistry with subsequent copy number analysis and IDH sequencing as a basis for an “integrated” diagnostic approach for adult astrocytoma, oligodendroglioma and glioblastoma , 2014, Acta Neuropathologica.

[21]  A. Lozano,et al.  Incidence of silent hemorrhage and delayed deterioration after stereotactic brain biopsy , 1998 .

[22]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[23]  J. Helpern,et al.  MRI quantification of non‐Gaussian water diffusion by kurtosis analysis , 2010, NMR in biomedicine.

[24]  S. Choi,et al.  Application of diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging for ganglioglioma in adults: Comparison study with oligodendroglioma. , 2016, Journal of neuroradiology. Journal de neuroradiologie.

[25]  Jong-Hee Chang,et al.  Differentiation between Primary Cerebral Lymphoma and Glioblastoma Using the Apparent Diffusion Coefficient: Comparison of Three Different ROI Methods , 2014, PloS one.

[26]  C. Zee,et al.  A diffusional kurtosis imaging study of idiopathic generalized epilepsy with unilateral interictal epileptiform discharges in children. , 2016, Journal of neuroradiology. Journal de neuroradiologie.

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

[28]  Eric Achten,et al.  Optimal Experimental Design for Diffusion Kurtosis Imaging , 2010, IEEE Transactions on Medical Imaging.

[29]  G. Tabatabai,et al.  ATRX immunostaining predicts IDH and H3F3A status in gliomas , 2016, Acta Neuropathologica Communications.

[30]  Pieter Wesseling,et al.  IDH mutant diffuse and anaplastic astrocytomas have similar age at presentation and little difference in survival: a grading problem for WHO , 2015, Acta Neuropathologica.

[31]  Contribution of the apparent diffusion coefficient in perilesional edema for the assessment of brain tumors. , 2008, Journal of neuroradiology. Journal de neuroradiologie.

[32]  J. Mehrkens,et al.  Novel Molecular Stereotactic Biopsy Procedures Reveal Intratumoral Homogeneity of Loss of Heterozygosity of 1p/19q and TP53 Mutations in World Health Organization Grade II Gliomas , 2009, Journal of neuropathology and experimental neurology.

[33]  Pieter Wesseling,et al.  International Society of Neuropathology‐Haarlem Consensus Guidelines for Nervous System Tumor Classification and Grading , 2014, Brain pathology.

[34]  Satoru Miyano,et al.  Mutational landscape and clonal architecture in grade II and III gliomas , 2015, Nature Genetics.

[35]  Glyn Johnson,et al.  Comparison of region‐of‐interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas , 2007, Journal of magnetic resonance imaging : JMRI.

[36]  R. Tanaka,et al.  Magnetic resonance imaging and histopathology of cerebral gliomas , 2004, Neuroradiology.

[37]  Wenzhen Zhu,et al.  Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation , 2015, Oncotarget.