MR Perfusion and Diffusion Values in Gliomas XIX Symposium Neuroradiologicum Perifocal MR Perfusion and Diffusion Values in Gliomas Z. Rumboldt, M.V. Spampinato, P. Morgan, C. Schiarelli, C. Rorden, J. Fridriksson Medical University of South Carolina Charleston, SC, USA

Background New therapies for gliomas highlight the limits of conventional imaging as a tool to assess tumor extension and progression Growing interest in advanced MR techniques such as perfusion and diffusion imaging to develop better ways to assess treatment response and prognosis Henson et al. AJNR Am J Neuroradiol. 2008 Mar; 29(3):419-24. Dhermain FG et. Lancet Neurol. 2010 Sep;9(9):906-20

MR Perfusion High grade gliomas characterized by increased neovascularization rCBV is a parameter correlating with the amount of capillaries within the tumor High-grade gliomas are characterised by an increased macrovasculature and microvasculature compared with those in healthy tissue. The amount of hyperperfusion is a marker of the biological behaviour and aggressiveness of the tumour, and the estimated relative cerebral blood volume (CBV) is a semiquantitative parameter that correlates with the amount of capillaries. Relative CBV can be used to assess perfusion characteristics (fi gure 2), and such perfusion imaging techniques require bolus tracking after gadolinium injection with a T2* dynamic susceptibility-contrast technique. Integration of the area under the signal curve gives an estimate of CBV, which correlates with increased vascularisation. Aronen, Neuroimaging Clin N Am. 2002 Nov;12(4):501-23

ADC Role of diffusion-weighted MRI in grading gliomas Pre- and post-treatment DWI data could assist in predicting response to anti-VEFG Arvinda HR et Al. J Neurooncol 2009; 94: 87–96 Pope WB et Al. Radiology 2009; 252: 182–89 Wu J et Al. J Clin Oncol 2010; 28 (suppl)

Purpose Validation Study Hypothesis: Measurements of rCBV and ADC within the tissue beyond the abnormality seen on conventional MR imaging may provide relevant information regarding the biological behavior of cerebral gliomas These data could help predicting the aggressiveness of a neoplasm, determining treatment response, and establishing prognosis

Materials and Methods 11 patients with cerebral glioma (5 WHO grade IV, 1 grade III, 5 grade II) – 6 progressed in 6 mos - 5 stable for 2 years - 4 oligodendroglial (3 low, 1 high grade) - 1.5T MR scanners (pre or post treatment) - ADC - rCBV

Materials and Methods rCBV analyzed off-line using the Java Image software package (www.xinapse.com) - Deconvolution of the arterial input function from the tissue response function according to the method described by Ostergaard et al. Ostergaard et Al. Magn. Reson. Med. 36:715-728 (1996).

Materials and Methods JIM software rCBV maps and rCBV maps processed using different vendors’ software were compared Image quality was rated in consensus by two neuroradiologists (random order, using a 3-point scale)

Materials and Methods -Tumor delineation: VOIs were drawn on all relevant FLAIR images using MRIcron (http://www.cabiatl.com/mricro/mricron) VOI overlaid on the ADC and rCBV maps

Materials and Methods In addition to the lesion VOI, mean values also obtained from incrementally dilated regions automatically defined using MRIcron software Dilation bands in 3D ranges away from the lesion at predefined distances: 0-4mm, 5-9mm, 10-14mm, 15-19mm Dilation constrained to ipsilateral hemisphere Ventricles excluded

Materials and Methods

MR (2/12/2010) 62 yo male with history of GBM with clinical progression 6 month prior to the MRI.

Materials and Methods Statistical Analysis Kruskal Wallis Grouping variable: progression rCBV ADC absolute values

Results rCBV images processed by Java Image software package: - superior in 6 cases - equal in 5 cases

Results

Results

Results Chi-Square p-value rCBV core 1.750 .186 rCBV 0-4mm 5.143 .023 7.000 .008 rCBV 10-14 mm rCBV 15-19 mm 2.893 .089 ADC core .000 1.000 ADC 0-4mm 5.143 .023 ADC 5-9mm 6.036 .014 ADC 10-14mm ADC 15-19mm 7.000 .008

Results - rCBV Progression Stable 1.29 0.69 0.9 3.23 0.83 0.7 0-4mm core 1.29 0.69 0.9 3.23 0.83 0.7 0-4mm 1.28 1.01 1.61 1.58 1.14 1.25 5-9mm 1.31 1.07 1.39 1.52 1.2 1.47 10-14mm 1.18 1.02 1.21 1.13 1.65 15-19mm 1.12 1 1.19 1.78 Stable core 0.64 0.71 0.97 0.58 0-4mm 0.82 0.86 1.03 0.76 5-9mm 0.88 0.95 1 0.75 10-14mm 0.79 1.04 0.68 15-19mm 0.85 1.12 1.08 0.72

Results - ADC Progression Stable Perifocal ADC values Dropping 0-4mm core 1.182 1.222 1.267 1.254 0.965 1.149 0-4mm 1.337 1.237 0.882 1.218 1.147 1.02 5-9mm 1.241 1.29 0.874 1.18 1.207 1.064 10-14mm 1.171 1.242 0.883 1.278 1.197 1.148 15-19mm 1.161 1.275 0.907 1.245 1.24 1.208 contralateral (core) 1.16 0.701 1.158 0.925 1.001 Stable core 1.275 1.118 1.564 1.267 1.214 0-4mm 1.099 0.995 1.375 0.882 1.124 5-9mm 1.084 0.962 1.103 0.874 1.007 10-14mm 0.999 1.067 0.883 1.029 15-19mm 1.008 0.959 1.075 0.907 1.054 contralateral (core) 1.03 0.924 0.701 0.998 Perifocal ADC values Dropping

Conclusion Java Image (JIM) software package can be used for brain MR perfusion analysis MRIcron may be a powerful tool for evaluation of perifocal tissue in brain gliomas ADC is routinely obtained, no postprocessing needed Perifocal absolute ADC values appear to discriminate as well as rCBV

Conclusion rCBV and ADC values in perifocal dilated regions may provide additional information that can assist in tissue characterization Further prospective investigations are necessary May make substantial contributions to treatment response and prognosis prediction