Whole tumor MR Perfusion histogram analysis in the preoperative assessment of patients with gliomas: Differentiation between high- and low-grade tumors

Vasileios K. Katsaros, Katerina Nikiforaki, Giorgos Manikis, Kostas Marias, Evangelia Liouta, Christos Boskos, George Kyriakopoulos, George Stranjalis, Nikolaos Papanikolaou

Abstract


Purpose: To compare the diagnostic accuracy of normalized Blood Volume (nBV) histogram metrics in differentiating low from high-grade gliomas.

Material and Methods: Forty-four patients (22 female, 22 male) with histologically confirmed gliomas were included. Group A comprised 10 patients with low grade gliomas (all grade II) while group B comprised 34 patients (4 grade III and 30 grade IV). Three-dimensional whole tumor segmentation was based on intensity level clustering in T2 FLAIR for the non-enhancing lesions or post contrast T1 weighted images for the enhancing lesions. Dynamic Susceptibility Contrast (DSC) perfusion was applied in all patients, and leakage corrected nBV maps were created. Corresponding histograms were generated from all the pixels included in the tumor volume. Minimum, maximum, mean, standard deviation, median, skewness, kurtosis, 5%, 30%, 70% and 95% percentiles, as well as normalized peak height and maximum peak position derived from normalized blood volume histograms were calculated for both groups. ROC analysis was performed to find optimum thresholds for differentiating between low and high grade gliomas.

Results: 5% percentile of nBV normalized histogram provided the highest area under the curve (AUROC: 0.93) for the differentiation of low from high grade gliomas. A threshold value of 0.07 for the 5% percentile of nBV normalized histogram resulted in 90.8% sensitivity and 90% specificity. The positive and negative predictive values were 96.7% and 75%, respectively while the accuracy reached 91%. When removing 2 IDH mutation positive HGG patients, the corresponding AUROC increased to 0.98.

Conclusion: Whole tumor normalized Blood Volume histogram analysis proved to be a very accurate method to differentiate low from high grade gliomas.


Keywords


brain tumors; perfusion; dynamic susceptibility weighted imaging; nBV

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References


Lev MH, Rosen BR. Clinical applications of intracranial perfusion MR imaging. Neuroimaging Clin N Am 1999; 9(2): 309-331.

Law M, Oh S, Babb JS, et al. Low-grade gliomas: Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging prediction of patient clinical response. Radiology 2006; 238(2): 658-667.

Covarrubias DJ, Rosen BR, Lev MH. Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 2004; 9(5): 528-537.

Yang D, Korogi Y, Sugahara T, et al. Cerebral gliomas: Prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy,echoplanar perfusion and diffusion-weighted MRI. Neuroradiology 2002; 44: 656-666.

Al-Okaili RN, Krejza J, Woo JH, et al. Intra-axial brain masses: MR imaging-based diagnostic strategy-initial experience. Radiology 2007; 243(2): 539-550.

Ebisu T, Tanaka C, Umeda M, et al. Discrimination of brain abscess from necrotic or cystic tumors by diffusion-weighted echo planar imaging. Magn Reson Imaging 1996; 14: 1113-1116.

Stadnik TW, Chaskis C, Michotte A, et al. Diffusion-weighted MR imaging of intracerebral masses: Comparison with conventional MR imaging and histologic findings. AJNR Am J Neuroradiol 2001; 22: 969-976.

Edelman RR, Mattle HP, Atkinson DJ, et al. Cerebral blood flow: Assessment with dynamic contrast-enhanced T2*-weighted MR imaging at 1.5 T. Radiology 1990; 176(1): 211-220.

Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps of gliomas: Comparison with tumor grade and histologic findings. Radiology 1994; 191(1): 41-51.

Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: Dynamic contrast-enhanced T2*- weighted MR imaging. Radiology 1999; 211(3): 791-798.

Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast enhanced MR: Confounding effect of elevated rCBV of oligodendrogliomas. AJNR Am J Neuroradiol 2004; 25(2): 214-222.

Emblem KE, Nedregaard B, Nome T, et al. Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps. Radiology 2008; 247: 808-817.

Rollin N, Guyotat J, Streichenberger N, et al. Clinical relevance of diffusion and perfusion magnetic resonance in assessing intra-axial brain tumors. Neuroradiology 2006; 48: 150-159.

Lee EJ, Lee SK, Agid R, et al. Preoperative grading of presumptive low-grade astrocytomas on MR imaging: Diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 2008; 29: 1872-1877.

McKnight TR, Lamborn KR, Love TD, et al. Correlation of magnetic resonance spectroscopic and growth characteristics within grade II and III gliomas. J Neurosurg 2007; 106: 660-666.

Law M, Yang S, Wang H, et al. Glioma grading: Sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003; 24: 1989-1998.

Kim HS, Kim SY. A prospective study on the added value of pulsed arterial spin labeling and apparent diffusion coefficients in the grading of gliomas. AJNR Am J Neuroradiol 2007; 28: 1693-1699.

Hakyemez B, Erdogan C, Ercan I, et al. High-grade and low-grade gliomas: Differentiation by using perfusion MR imaging. Clin Radiol 2005; 60: 493-502.

Barajas RF Jr, Cha S. Benefits of dynamic susceptibility-weighted contrast-enhanced perfusion MRI for glioma diagnosis and therapy. CNS Oncol 2014; 3(6): 407-419.

Zonari P, Baraldi P, Crisi G. Multimodal MRI in the characterization of glial neoplasms: The combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging. Neuroradiology 2007; 49: 795-803.

Law M, Yang S, Babb JS, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25: 746-755.

Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006; 27: 859-867.

Emblem KE, Scheie D, Due-Tonnessen P.Histogram Analysis of MR Imaging–Derived Cerebral Blood Volume Maps: Combined Glioma Grading and Identification of Low-Grade Oligodendroglial Subtypes. AJNR 2008; 29: 1664-1670.

Yan H, Parsons DW, Jin G, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009; 360: 765-773.

Dang L, White DW, Gross S, et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 2009; 462: 739-744.


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