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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

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.

Keywords

Glioblastoma MRI Diffusion models Biomarkers 

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

Notes

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).

Supplementary material

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Supplementary material 1 (TIFF 4536 KB)
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Supplementary material 2 (XLSX 35 KB)
11060_2018_2910_MOESM3_ESM.docx (12 kb)
Supplementary material 3 (DOCX 11 KB)

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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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