Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High-b-Value Diffusion-weighted MR Imaging—Correlation with Tumor Grade

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Our results suggest that histogram analysis based on apparent diffusion coefficients of entire tumor volumes can be a useful and objective diagnostic tool for grading gliomas.


To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on entire tumor volume data in determining glioma grade and to evaluate the diagnostic performance of ADC maps at standard (1000 sec/mm2) and high (3000 sec/mm2) b values.

Materials and Methods

This retrospective study was approved by the institutional review board, and informed consent was waived. Twenty-seven patients with astrocytic tumors underwent diffusion-weighted magnetic resonance imaging with b values of 1000 and 3000 sec/mm2, and the corresponding ADC maps were calculated (ADC1000 and ADC3000, respectively). Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and were summated to derive volume-based data of the entire tumor. Histogram parameters were correlated with tumor grade by using repeated measurements analysis of variance, the Tukey-Kramer test for post hoc comparisons, and an unpaired Student t test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for each histogram parameter, and sensitivity and specificity were assessed.


Minimum ADC1000 and ADC3000 both decreased with increasing tumor grade. The 50th and 75th percentiles of cumulative ADC1000 histograms showed significant differences between grades (P = .015 and .001, respectively), while the fifth and 75th percentiles of cumulative ADC3000 histograms showed such differences (P = .015 and .014, respectively). Minimum ADC and the fifth percentile for both ADC1000 (P < .001 and P = .024, respectively) and ADC3000 (P < .001 and P = .001, respectively) proved to be significant histogram parameters for differentiating high- from low-grade gliomas. The diagnostic value of the parameters derived from ADC1000 and ADC3000 were compared, and a significant difference (0.202, P = .014) was found between the areas under the ROC curve of the fifth percentiles for ADC1000 and ADC3000.


Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas.

© RSNA, 2011

Supplemental material:


  • 1 Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M. Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol 2006;2(9):494–503; quiz 1 p following 516. Crossref, MedlineGoogle Scholar
  • 2 Sugahara T, Korogi Y, Kochi M, et al.. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 1999;9(1):53–60. Crossref, MedlineGoogle Scholar
  • 3 Watanabe M, Tanaka R, Takeda N. Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 1992;34(6):463–469. Crossref, MedlineGoogle Scholar
  • 4 Brant-Zawadzki M. Pitfalls of contrast-enhanced imaging in the nervous system. Magn Reson Med 1991;22(2):243–248. Crossref, MedlineGoogle Scholar
  • 5 Brant-Zawadzki M, Berry I, Osaki L, Brasch R, Murovic J, Norman D. Gd-DTPA in clinical MR of the brain. I. Intraaxial lesions. AJR Am J Roentgenol 1986;147(6):1223–1230. Crossref, MedlineGoogle Scholar
  • 6 Murakami R, Hirai T, Kitajima M, et al.. Magnetic resonance imaging of pilocytic astrocytomas: usefulness of the minimum apparent diffusion coefficient (ADC) value for differentiation from high-grade gliomas. Acta Radiol 2008;49(4):462–467. Crossref, MedlineGoogle Scholar
  • 7 Provenzale JM, Mukundan S, Barboriak DP. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 2006;239(3):632–649. LinkGoogle Scholar
  • 8 Murakami R, Hirai T, Sugahara T, et al.. Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. Radiology 2009;251(3):838–845. LinkGoogle Scholar
  • 9 Tozer DJ, Jäger HR, Danchaivijitr N, et al.. Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 2007;20(1):49–57. Crossref, MedlineGoogle Scholar
  • 10 Arvinda HR, Kesavadas C, Sarma PS, et al.. Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 2009;94(1):87–96. Crossref, MedlineGoogle Scholar
  • 11 Lee EJ, Lee SK, Agid R, Bae JM, Keller A, Terbrugge K. Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 2008;29(10):1872–1877. Crossref, MedlineGoogle Scholar
  • 12 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(10):795–803. Crossref, MedlineGoogle Scholar
  • 13 Catalaa I, Henry R, Dillon WP, et al.. Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas. NMR Biomed 2006;19(4):463–475. Crossref, MedlineGoogle Scholar
  • 14 Kono K, Inoue Y, Nakayama K, et al.. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 2001;22(6):1081–1088. MedlineGoogle Scholar
  • 15 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(8):656–666. Crossref, MedlineGoogle Scholar
  • 16 Lam WW, Poon WS, Metreweli C. Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin Radiol 2002;57(3):219–225. Crossref, MedlineGoogle Scholar
  • 17 Cha S. Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 2006;27(3):475–487. MedlineGoogle Scholar
  • 18 Kondziolka D, Lunsford LD, Martinez AJ. Unreliability of contemporary neurodiagnostic imaging in evaluating suspected adult supratentorial (low-grade) astrocytoma. J Neurosurg 1993;79(4):533–536. Crossref, MedlineGoogle Scholar
  • 19 Knopp EA, Cha S, Johnson G, et al.. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 1999;211(3):791–798. LinkGoogle Scholar
  • 20 Abramoff M, Magelhães P, Ram S. Image processing with ImageJ. Biophoton Int 2004;11(7):36–42. Google Scholar
  • 21 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(3):808–817. LinkGoogle Scholar
  • 22 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44(3):837–845. Crossref, MedlineGoogle Scholar
  • 23 Kitis O, Altay H, Calli C, Yunten N, Akalin T, Yurtseven T. Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol 2005;55(3):393–400. Crossref, MedlineGoogle Scholar
  • 24 Higano S, Yun X, Kumabe T, et al.. Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 2006;241(3):839–846. LinkGoogle Scholar
  • 25 DeLano MC, Cooper TG, Siebert JE, Potchen MJ, Kuppusamy K. High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 2000;21(10):1830–1836. MedlineGoogle Scholar
  • 26 Niendorf T, Dijkhuizen RM, Norris DG, van Lookeren Campagne M, Nicolay K. Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn Reson Med 1996;36(6):847–857. Crossref, MedlineGoogle Scholar
  • 27 Mulkern RV, Gudbjartsson H, Westin CF, et al.. Multi-component apparent diffusion coefficients in human brain. NMR Biomed 1999;12(1):51–62. Crossref, MedlineGoogle Scholar
  • 28 Maier SE, Bogner P, Bajzik G, et al.. Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging. Radiology 2001;219(3):842–849. LinkGoogle Scholar
  • 29 Seo HS, Chang KH, Na DG, Kwon BJ, Lee DH. High b-value diffusion (b = 3000 s/mm2) MR imaging in cerebral gliomas at 3T: visual and quantitative comparisons with b = 1000 s/mm2. AJNR Am J Neuroradiol 2008;29(3):458–463. Crossref, MedlineGoogle Scholar
  • 30 Price SJ, Jena R, Burnet NG, et al.. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 2006;27(9):1969–1974. MedlineGoogle Scholar
  • 31 Grier JT, Batchelor T. Low-grade gliomas in adults. Oncologist 2006;11(6):681–693. Crossref, MedlineGoogle Scholar
  • 32 Padhani AR, Liu G, Koh DM, et al.. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11(2):102–125. Crossref, MedlineGoogle Scholar
  • 33 Kim HS, Kim JH, Kim SH, Cho KG, Kim SY. Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence. Radiology 2010;256(3):906–915. LinkGoogle Scholar
  • 34 Law M, Young R, Babb J, Pollack E, Johnson G. Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 2007;28(4):761–766. MedlineGoogle Scholar

Article History

Received April 3, 2011; revision requested May 5; revision received June 13; accepted June 23; final version accepted July 14.
Published online: Dec 2011
Published in print: Dec 2011