Journal of Neuro-Oncology

, Volume 139, Issue 3, pp 651–659 | Cite as

Mono-exponential, diffusion kurtosis and stretched exponential diffusion MR imaging response to chemoradiation in newly diagnosed glioblastoma

  • Ararat Chakhoyan
  • Davis C. Woodworth
  • Robert J. Harris
  • Albert Lai
  • Phioanh L. Nghiemphu
  • Linda M. Liau
  • Whitney B. Pope
  • Timothy F. Cloughesy
  • Benjamin M. Ellingson
Clinical Study



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).


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).


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.


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.


Glioblastoma MRI Diffusion models Biomarkers 



Magnetic resonance imaging


Diffusion weighted imaging


Apparent diffusion coefficient


Diffusion corrected apparent diffusion coefficient


Apparent kurtosis coefficient


Distributed diffusion coefficient

α (alpha)

Intra-voxel diffusion heterogeneity


Normal appearing white matter


Contrast-enhancing tumor





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).

Supplementary material

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  1. 1.
    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):1725–1731CrossRefGoogle Scholar
  2. 2.
    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):459–466CrossRefPubMedGoogle Scholar
  3. 3.
    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):4–27CrossRefPubMedGoogle Scholar
  4. 4.
    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):1963–1972CrossRefPubMedGoogle Scholar
  5. 5.
    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 9:53–60CrossRefPubMedGoogle Scholar
  6. 6.
    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 12:89–99CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    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):538–548CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    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):e79008CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    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):434–442CrossRefPubMedGoogle Scholar
  10. 10.
    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):5524–5529CrossRefPubMedGoogle Scholar
  11. 11.
    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):333–343CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ellingson BM, Cloughesy TF et al (2013) Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with radiochemotherapy. Neuro Oncol 15(3):382–390CrossRefPubMedGoogle Scholar
  13. 13.
    Stejskal EO, Tanner JE (1964) Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42:288–292CrossRefGoogle Scholar
  14. 14.
    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):401–407CrossRefPubMedGoogle Scholar
  15. 15.
    Le Bihan D, Johansen-Berg H (2012) Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage 61(2):423–441CrossRefGoogle Scholar
  16. 16.
    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):1432–1440CrossRefPubMedGoogle Scholar
  17. 17.
    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):727–734CrossRefPubMedGoogle Scholar
  18. 18.
    Kwee TC, Galbán CJ et al (2010) Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas. NMR Biomed 23(2):179–187PubMedPubMedCentralGoogle Scholar
  19. 19.
    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):496–504CrossRefPubMedGoogle Scholar
  20. 20.
    Falk DA, Nilsson M et al (2017) Glioma grade discrimination with MR diffusion kurtosis imaging: A meta-analysis of diagnostic accuracy. Radiology 4:171315Google Scholar
  21. 21.
    Jensen JH, Helpern JA (2010) MRI quantification of non-gaussian water diffusion by kurtosis analysis. NMR Biomed 23(7):698–710CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    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):908–916CrossRefGoogle Scholar
  23. 23.
    Ellingson BM, Bendszus M et al (2015) Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 17(9):1188–1198PubMedPubMedCentralGoogle Scholar
  24. 24.
    Ellingson BM, Nguyen HN et al (2016) Contrast-enhancing tumor growth dynamics of preoperative, treatment-naive human glioblastoma. Cancer 122(11):1718–1727CrossRefPubMedGoogle Scholar
  25. 25.
    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):515–534CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    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 134:177–188CrossRefGoogle Scholar
  27. 27.
    Pope WB, Kim et al (2009) Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 252(1):182–189CrossRefPubMedGoogle Scholar
  28. 28.
    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):491–498CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    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):5745–5756CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Padhani AR, Liu G et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11(2):102–125CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    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):2252–2254CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Zhu J, Zhuo et al (2015) Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia. Neuroimage Clin 7:170–176CrossRefPubMedGoogle Scholar
  33. 33.
    Raab P, Hattingen E et al (2010) Cerebral gliomas: Diffusional kurtosis imaging analysis of microstructural differences. Radiology 254(3):876–881CrossRefPubMedGoogle Scholar
  34. 34.
    Van Cauter S, Veraart J et al (2012) Gliomas: Diffusion kurtosis MR imaging in grading. Radiology 263(2):492–501CrossRefPubMedGoogle Scholar
  35. 35.
    Jiang R, Jiang J et al (2015) Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget 6(39):42380–42393PubMedPubMedCentralGoogle Scholar
  36. 36.
    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):531–537CrossRefGoogle Scholar
  37. 37.
    Li Y, Lupo JM et al (2011) Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. Neuro Oncol 13(5):546–557CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ararat Chakhoyan
    • 1
    • 2
  • Davis C. Woodworth
    • 1
    • 2
    • 5
  • Robert J. Harris
    • 1
    • 2
  • Albert Lai
    • 4
    • 6
  • Phioanh L. Nghiemphu
    • 4
    • 6
  • Linda M. Liau
    • 7
  • Whitney B. Pope
    • 2
  • Timothy F. Cloughesy
    • 4
    • 6
  • Benjamin M. Ellingson
    • 1
    • 2
    • 3
    • 4
    • 8
  1. 1.UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging BiomarkersUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Radiological Sciences, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA
  3. 3.Department of Bioengineering, Henry Samueli School of Engineering and Applied ScienceUniversity of California, Los AngelesLos AngelesUSA
  4. 4.UCLA Neuro-Oncology ProgramUniversity of California, Los AngelesLos AngelesUSA
  5. 5.Department of Biomedical Physics, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA
  6. 6.Department of Neurology, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA
  7. 7.Department of Neurosurgery, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA
  8. 8.UCLA Brain Tumor Imaging Laboratory (BTIL), Biomedical Physics, Psychiatry, and Bioengineering, Departments of Radiological Sciences and Psychiatry, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA

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