European Radiology

, Volume 25, Issue 6, pp 1708–1713 | Cite as

Intravoxel water diffusion heterogeneity MR imaging of nasopharyngeal carcinoma using stretched exponential diffusion model

  • Vincent LaiEmail author
  • Victor Ho Fun Lee
  • Ka On Lam
  • Henry Chun Kin Sze
  • Queenie Chan
  • Pek Lan Khong
Head and Neck



To determine the utility of stretched exponential diffusion model in characterisation of the water diffusion heterogeneity in different tumour stages of nasopharyngeal carcinoma (NPC).

Materials and methods

Fifty patients with newly diagnosed NPC were prospectively recruited. Diffusion-weighted MR imaging was performed using five b values (0–2,500 s/mm2). Respective stretched exponential parameters (DDC, distributed diffusion coefficient; and alpha (α), water heterogeneity) were calculated. Patients were stratified into low and high tumour stage groups based on the American Joint Committee on Cancer (AJCC) staging for determination of the predictive powers of DDC and α using t test and ROC curve analyses.


The mean ± standard deviation values were DDC = 0.692 ± 0.199 (×10−3 mm2/s) for low stage group vs 0.794 ± 0.253 (×10−3 mm2/s) for high stage group; α = 0.792 ± 0.145 for low stage group vs 0.698 ± 0.155 for high stage group. α was significantly lower in the high stage group while DDC was negatively correlated. DDC and α were both reliable independent predictors (p < 0.001), with α being more powerful. Optimal cut-off values were (sensitivity, specificity, positive likelihood ratio, negative likelihood ratio) DDC = 0.692 × 10−3 mm2/s (94.4 %, 64.3 %, 2.64, 0.09), α = 0.720 (72.2 %, 100 %, −, 0.28).


The heterogeneity index α is robust and can potentially help in staging and grading prediction in NPC.

Key Points

Stretched exponential diffusion models can help in tissue characterisation in nasopharyngeal carcinoma

α and distributed diffusion coefficient (DDC) are negatively correlated

α is a robust heterogeneity index marker

α can potentially help in staging and grading prediction


Nasopharyngeal carcinoma Diffusion weighted imaging Magnetic resonance imaging Stretched exponential Staging 



Alpha (intravoxel water diffusion heterogeneity)


American Joint Committee on Cancer


Area under curve


Distributed diffusion coefficient






Intravoxel incoherent motion


Magnetic resonance


Nasopharyngeal carcinoma


Positron emission tomography with computed tomography


Receiver operating characteristic




Standard deviation


Signal-to-noise ratio


Spectral presaturation inversion recovery


Short TI inversion recovery




Repetition time/echo time


Turbo spin echo



The scientific guarantor of this publication is Prof. Khong Pek Lan. The authors of this manuscript declare relationships with the following companies: Dr. Q Chan is currently employed by Philips Medial Systems. This study has received funding by University Grants Council (UGC) seed funding from The University of Hong Kong, project no. 201112159010.

No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Approval from the institutional animal care committee was not required because this study did not involve animals. Study subjects or cohorts have not been previously reported.

Methodology: prospective, diagnostic or prognostic study, performed at one institution.


  1. 1.
    Lai V, Khong PL (2014) Updates on MR imaging and 18F-FDG PET/CT imaging in nasopharyngeal carcinoma. Oral Oncol 50:539–548CrossRefPubMedGoogle Scholar
  2. 2.
    Lai V, Li X, Lee VHF, Lam KO, Chan Q, Khong PL (2013) Intravoxel incoherent motion MR imaging: comparison of diffusion and perfusion characteristics between nasopharyngeal carcinoma and post-chemoradiation fibrosis. Eur Radiol 23:2793–2801CrossRefPubMedGoogle Scholar
  3. 3.
    Lai V, Li X, Lee VHF et al (2014) Nasopharyngeal carcinoma: comparison of diffusion and perfusion characteristics between different tumour stages using intravoxel incoherent motion MR imaging. Eur Radiol 24:176–183CrossRefPubMedGoogle Scholar
  4. 4.
    Hauser T, Essig M, Jensen A et al (2013) Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-base intravoxel incoherent motion parameters – preliminary results. Neuroradiology 55:527–536CrossRefPubMedGoogle Scholar
  5. 5.
    Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505CrossRefPubMedGoogle Scholar
  6. 6.
    Riches SF, Hawtin K, Charles-Edwards EM, de Souza NM (2009) Diffusion-weighted imaging of the prostate and rectal wall: comparison of biexponential and monoexponential modeled diffusion and associated perfusion coefficients. NMR Biomed 22:318–325CrossRefPubMedGoogle Scholar
  7. 7.
    Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS (2003) Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med 50:727–734CrossRefPubMedGoogle Scholar
  8. 8.
    Bennett KM, Hyde JS, Rand SD et al (2004) Intravoxel distribution of DWI decay rates reveals C6 glioma invasion in rat brain. Magn Reson Med 52:994–1004CrossRefPubMedGoogle Scholar
  9. 9.
    Bennett KM, Hyde JS, Schmainda KM (2006) Water diffusion heterogeneity index in the human brain is insensitive to the orientation of applied magnetic field gradients. Magn Reson Med 56:235–239CrossRefPubMedGoogle Scholar
  10. 10.
    Hall MG, Barrick TR (2008) From diffusion-weighted MRI to anomalous diffusion imaging. Magn Reson Med 59:447–455CrossRefPubMedGoogle Scholar
  11. 11.
    Mazaheri Y, Afaq A, Rowe DB, Lu Y, Shukla-Dave A, Grover J (2012) Diffusion-weighted magnetic resonance imaging of the prostate: improved robustness with stretched exponential modeling. J Comput Assist Tomogr 36:695–703CrossRefPubMedGoogle Scholar
  12. 12.
    Lu Y, Jansen JF, Mazaheri Y, Stambuk HE, Koutcher JA, Shukla-Dave A (2012) Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer. J Magn Reson Imaging 36:1088–1096CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Jansen JF, Stambuk HE, Koutcher JA, Shukla-Dave A (2010) Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol 31:741–748CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Vandecaveye V, De Keyzer F, Dirix P, Lambrecht M, Nuyts S, Hermans R (2010) Applications of diffusion-weighted magnetic resonance imaging in head and neck squamous cell carcinoma. Neuroradiology 52:773–784CrossRefPubMedGoogle Scholar
  15. 15.
    Kwee T, Galban CJ, Tsien C et al (2010) Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas. NMR Biomed 23:179–187PubMedCentralPubMedGoogle Scholar
  16. 16.
    Provenzale JM, Mukundan S, Barboriak DP (2006) Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 239:632–649CrossRefPubMedGoogle Scholar
  17. 17.
    Braithwaite AC, Dale BM, Boll DT, Merkle EM (2009) Short- and midterm reproducibility of apparent diffusion coefficient measurements at 3.0-T diffusion-weighted imaging of the abdomen. Radiology 250:459–465CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2014

Authors and Affiliations

  • Vincent Lai
    • 1
    Email author
  • Victor Ho Fun Lee
    • 2
  • Ka On Lam
    • 2
  • Henry Chun Kin Sze
    • 2
  • Queenie Chan
    • 3
  • Pek Lan Khong
    • 1
  1. 1.Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Queen Mary HospitalUniversity of Hong KongPok Fu LamHong Kong
  2. 2.Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, Queen Mary HospitalUniversity of Hong KongPok Fu LamHong Kong
  3. 3.Philips Healthcare, Hong KongShatinHong Kong

Personalised recommendations