Abstract
Purpose
To quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM).
Methods
Diffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7 days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS).
Results
Diffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS.
Conclusions
Despite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.
Similar content being viewed by others
Abbreviations
- MRI:
-
Magnetic resonance imaging
- DWI:
-
Diffusion weighted imaging
- ADC:
-
Apparent diffusion coefficient
- Dapp:
-
Diffusion corrected apparent diffusion coefficient
- Kapp:
-
Apparent kurtosis coefficient
- DDC:
-
Distributed diffusion coefficient
- α (alpha):
-
Intra-voxel diffusion heterogeneity
- NAWM:
-
Normal appearing white matter
- CET:
-
Contrast-enhancing tumor
- GBM:
-
Glioblastoma
References
Walker MD, Strike TA, Sheline GE (1979) An analysis of dose-effect relationship in the radiotherapy of malignant gliomas. Int J Rad Oncol Biol Phys 5(10):1725–1731
Stupp R, Hegi ME et al (2009) 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. Lancet Oncol 10(5):459–466
Weller M, Cloughesy T, Perry JR, Wick W (2013) Standards of care for treatment of recurrent glioblastoma—are we there yet? Neuro Oncol 15(1):4–27
Wen PY, Macdonald DR et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in Neuro-oncology Working Group. J Clin Oncol 28(11):1963–1972
Sugahara T, Korogi Y et al (1999) Usefulness of Diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9:53–60
Simon D, Fritzsche KH et al (2012) Diffusion-weighted imaging-based probabilistic segmentation of high- and low-proliferative areas in high-grade gliomas. Cancer Imaging 12:89–99
Ellingson BM, Malkin MG et al (2010) Validation of Functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 31(3):538–548
Chen L, Liu M et al (2013) The correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-analysis. PLoS ONE 8(11):e79008
Karavaeva E, Harris RJ et al (2015) Relationship between [18F]-FDOPA PET uptake, apparent diffusion coefficient (ADC), and proliferation rate in recurrent malignant gliomas. Mol Imaging Biol 17(3):434–442
Moffat BA, Chenevert TL et al (2005) Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci USA 102(15):5524–5529
Ellingson BM, Cloughesy TF et al (2012) Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma. Neuro Oncol 14(3):333–343
Ellingson BM, Cloughesy TF et al (2013) Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with radiochemotherapy. Neuro Oncol 15(3):382–390
Stejskal EO, Tanner JE (1964) Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42:288–292
Le Bihan D, Breton E et al (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161(2):401–407
Le Bihan D, Johansen-Berg H (2012) Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage 61(2):423–441
Jensen JH, Helpern JA et al (2005) Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53(6):1432–1440
Bennett KM, Schmainda KM et al (2003) Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med 50(4):727–734
Kwee TC, Galbán CJ et al (2010) Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas. NMR Biomed 23(2):179–187
Bai Y, Lin Y et al (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278(2):496–504
Falk DA, Nilsson M et al (2017) Glioma grade discrimination with MR diffusion kurtosis imaging: A meta-analysis of diagnostic accuracy. Radiology 4:171315
Jensen JH, Helpern JA (2010) MRI quantification of non-gaussian water diffusion by kurtosis analysis. NMR Biomed 23(7):698–710
Ellingson BM, Cloughesy TF et al (2012) Anatomic localization of O6-methylguanine DNA methyltransferase (MGMT) promoter methylated and unmethylated tumors: A radiographic study in 358 de novo human glioblastomas. NeuroImage 16(2):908–916
Ellingson BM, Bendszus M et al (2015) Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 17(9):1188–1198
Ellingson BM, Nguyen HN et al (2016) Contrast-enhancing tumor growth dynamics of preoperative, treatment-naive human glioblastoma. Cancer 122(11):1718–1727
Verma N, Cowperthwaite MC, Burnett MG, Markey MK (2013) Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol 15(5):515–534
Leu K, Ott GA et al (2016) Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II–III diffuse gliomas. J Neurooncol 134:177–188
Pope WB, Kim et al (2009) Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 252(1):182–189
Pope WB, Qiao XJ et al (2012) Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol 108(3):491–498
Ellingson BM, Gerstner E et al (2017) Diffusion MRI phenotypes predict overall survival benefit from anti-VEGF monotherapy in recurrent glioblastoma: converging evidence from phase II trials. Clin Cancer Res 23(19):5745–5756
Padhani AR, Liu G et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11(2):102–125
Cheung JS, Wang E, Lo EH, Sun PZ (2012) Stratification of heterogeneous diffusion MRI ischemic lesion with kurtosis imaging: evaluation of mean diffusion and kurtosis MRI mismatch in an animal model of transient focal ischemia. Stroke 43(8):2252–2254
Zhu J, Zhuo et al (2015) Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia. Neuroimage Clin 7:170–176
Raab P, Hattingen E et al (2010) Cerebral gliomas: Diffusional kurtosis imaging analysis of microstructural differences. Radiology 254(3):876–881
Van Cauter S, Veraart J et al (2012) Gliomas: Diffusion kurtosis MR imaging in grading. Radiology 263(2):492–501
Jiang R, Jiang J et al (2015) Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget 6(39):42380–42393
Kwee TC, Galbán CJ et al (2011) Comparison of apparent diffusion coefficients (ADC) and distributed diffusion coefficients (DDC) in high-grade gliomas. J Magn Reson Imaging 31(3):531–537
Li Y, Lupo JM et al (2011) Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. Neuro Oncol 13(5):546–557
Funding
National Brain Tumor Society (NBTS) Research Grant (Ellingson, Cloughesy); American Cancer Society (ACS) Research Scholar Grant (RSG-15-003-01-CCE) (Ellingson); Art of the Brain (Cloughesy); UCLA SPORE in Brain Cancer (NIH/NCI 1P50CA211015-01A1) (Ellingson, Cloughesy); NIH/NCI 1R21CA223757-01 (Ellingson); NIH/NCI 1R21CA167354 (Ellingson).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Chakhoyan, A., Woodworth, D.C., Harris, R.J. et al. Mono-exponential, diffusion kurtosis and stretched exponential diffusion MR imaging response to chemoradiation in newly diagnosed glioblastoma. J Neurooncol 139, 651–659 (2018). https://doi.org/10.1007/s11060-018-2910-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11060-018-2910-9