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Advances in Therapy

, Volume 33, Issue 7, pp 1158–1168 | Cite as

The Role of Intravoxel Incoherent Motion MRI in Predicting Early Treatment Response to Chemoradiation for Metastatic Lymph Nodes in Nasopharyngeal Carcinoma

  • Liyan Lu
  • Yuehua LiEmail author
  • Wenbin LiEmail author
Original Research

Abstract

Introduction

Pilot studies have suggested potential clinical applications for intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in head and neck cancers. This study aimed to characterize metastatic lymph nodes using IVIM MRI, and to evaluate the role of IVIM MRI in the prediction of the early treatment response of lymph node metastasis from nasopharyngeal carcinoma (NPC).

Methods

A total of 122 patients with metastatic lymph nodes from NPC underwent two MRI examinations, pre-treatment and post-treatment (at 4 weeks and at ≥2 years from the end of chemoradiotherapy). Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1. Differences in the initial IVIM parameters [pure molecular diffusion (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f)] between nodes with a partial response (PR) and a complete response (CR) were analyzed in 102 patients after the exclusion of 20.

Results

The initial D*, D, and apparent diffusion coefficient (ADC) did not reveal a significant difference between nodes showing a PR or a CR. The mean initial f value was significantly higher in patients with a PR relative to patients with a CR (p = 0.003), and its sensitivity and specificity in predicting treatment response to chemoradiotherapy were 86.7% and 100%, respectively.

Conclusions

The present study indicated that the initial f value may be more accurate than the initial D*, D, and ADC in the early prediction of treatment response to chemoradiotherapy for metastatic lymph nodes in patients with NPC.

Keywords

Nasopharyngeal carcinoma Intravoxel incoherent motion MRI Treatment response 

Notes

Acknowledgments

The article processing charges for this publication were funded by grants from the National Natural Scientific Foundation of China, No. 81471656; Shanghai Science Foundation, No,14 ZR 1432100. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work as a whole, and have given final approval for the version to be published. Medical writing assistance was provided by Liwen Bianji company. This assistance was funded by Liyan Lu. The authors thank Shiteng Suo from department of radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University for their helpful contribution towards this manuscript.

Disclosures

Liyan Lu, Yuehua Li and Wenbin Li have nothing to disclose.

Compliance with Ethics Guidelines

The study was approved by the Institutional Review Board (No. 2013-06-28) of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964, as revised in 2013. Informed consent was obtained from all patients for being included in the study.

References

  1. 1.
    Wei WI, Sham JS. Nasopharyngeal carcinoma. Lancet (London, England). 2005;365(9476):2041–54.CrossRefGoogle Scholar
  2. 2.
    Ho FC, Tham IW, Earnest A, Lee KM, Lu JJ. Patterns of regional lymph node metastasis of nasopharyngeal carcinoma: a meta-analysis of clinical evidence. BMC Cancer. 2012;12:98.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Zheng J, Li J, Xu L, Xie G, Wen Q, Luo J, Li D, Huang D, Fan S. Phosphorylated Mnk1 and eIF4E are associated with lymph node metastasis and poor prognosis of nasopharyngeal carcinoma. PLoS One. 2014;9(2):e89220.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Dunne AA, Muller HH, Eisele DW, Kessel K, Moll R, Werner JA. Meta-analysis of the prognostic significance of perinodal spread in head and neck squamous cell carcinomas (HNSCC) patients. Eur J Cancer (Oxford, England: 1990). 2006;42(12):1863–8.CrossRefGoogle Scholar
  5. 5.
    Razek AA, Elsorogy LG, Soliman NY, Nada N. Dynamic susceptibility contrast perfusion MR imaging in distinguishing malignant from benign head and neck tumors: a pilot study. Eur J Radiol. 2011;77(1):73–9.CrossRefPubMedGoogle Scholar
  6. 6.
    Lee FK, King AD, Ma BB, Yeung DK. Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) for differential diagnosis in head and neck cancers. Eur J Radiol. 2012;81(4):784–8.CrossRefPubMedGoogle Scholar
  7. 7.
    Jia QJ, Zhang SX, Chen WB, Liang L, Zhou ZG, Qiu QH, Liu ZY, Zeng QX, Liang CH. Initial experience of correlating parameters of intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging at 3.0 T in nasopharyngeal carcinoma. Eur Radiol. 2014;24(12):3076–87.CrossRefPubMedGoogle Scholar
  8. 8.
    Barchetti F, Pranno N, Giraldi G. The role of 3 Tesla diffusion-weighted imaging in the differential diagnosis of benign versus malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Biomed Res Int. 2014;2014:532095.PubMedPubMedCentralGoogle Scholar
  9. 9.
    de Bondt RB, Hoeberigs MC, Nelemans PJ, Deserno WM, Peutz-Kootstra C, Kremer B, Beets-Tan RG. Diagnostic accuracy and additional value of diffusion-weighted imaging for discrimination of malignant cervical lymph nodes in head and neck squamous cell carcinoma. Neuroradiology. 2009;51(3):183–92.CrossRefPubMedGoogle Scholar
  10. 10.
    King AD, Ahuja AT, Yeung DK, Fong DK, Lee YY, Lei KI, Tse GM. Malignant cervical lymphadenopathy: diagnostic accuracy of diffusion-weighted MR imaging. Radiology. 2007;245(3):806–13.CrossRefPubMedGoogle Scholar
  11. 11.
    Sumi M, Sakihama N, Sumi T, Morikawa M, Uetani M, Kabasawa H, Shigeno K, Hayashi K, Takahashi H, Nakamura T. Discrimination of metastatic cervical lymph nodes with diffusion-weighted MR imaging in patients with head and neck cancer. AJNR Am J Neuroradiol. 2003;24(8):1627–34.PubMedGoogle Scholar
  12. 12.
    Hauser T, Essig M, Jensen A, Gerigk L, Laun FB, Munter M, Simon D, Stieltjes B. Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters-preliminary results. Neuroradiology. 2013;55(5):527–36.CrossRefPubMedGoogle Scholar
  13. 13.
    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer (Oxford, England: 1990). 2009;45(2):228–47.CrossRefGoogle Scholar
  14. 14.
    Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, Hua J, Xu J. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve-fitting methods. J Magn Reson Imaging JMRI. 2015;42(2):362–70.CrossRefPubMedGoogle Scholar
  15. 15.
    Le Bihan D. Intravoxel incoherent motion imaging using steady-state free precession. Magn Reson Med. 1988;7(3):346–51.CrossRefPubMedGoogle Scholar
  16. 16.
    Hauser T, Essig M, Jensen A, Laun FB, Munter M, Maier-Hein KH, Stieltjes B. Prediction of treatment response in head and neck carcinomas using IVIM-DWI: evaluation of lymph node metastasis. Eur J Radiol. 2014;83(5):783–7.CrossRefPubMedGoogle Scholar
  17. 17.
    Lu Y, Jansen JF, Mazaheri Y, Stambuk HE, Koutcher JA, Shukla-Dave A. Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer. J Magn Reson Imaging JMRI. 2012;36(5):1088–96.CrossRefPubMedGoogle Scholar
  18. 18.
    Zhang SX, Jia QJ, Zhang ZP, Liang CH, Chen WB, Qiu QH, Li H. Intravoxel incoherent motion MRI: emerging applications for nasopharyngeal carcinoma at the primary site. Eur Radiol. 2014;24(8):1998–2004.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Lemke A, Laun FB, Simon D, Stieltjes B, Schad LR. An in vivo verification of the intravoxel incoherent motion effect in diffusion-weighted imaging of the abdomen. Magn Reson Med. 2010;64(6):1580–5.CrossRefPubMedGoogle Scholar
  20. 20.
    Cao Y, Popovtzer A, Li D, Chepeha DB, Moyer JS, Prince ME, Worden F, Teknos T, Bradford C, Mukherji SK, et al. Early prediction of outcome in advanced head-and-neck cancer based on tumor blood volume alterations during therapy: a prospective study. Int J Radiat Oncol Biol Phys. 2008;72(5):1287–90.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Chawla S, Kim S, Dougherty L, Wang S, Loevner LA, Quon H, Poptani H. Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck. AJR Am J Roentgenol. 2013;200(1):35–43.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Hayano K, Okazumi S, Shuto K, Matsubara H, Shimada H, Nabeya Y, Kazama T, Yanagawa N, Ochiai T. Perfusion CT can predict the response to chemoradiation therapy and survival in esophageal squamous cell carcinoma: initial clinical results. Oncol Rep. 2007;18(4):901–8.PubMedGoogle Scholar
  23. 23.
    Zima A, Carlos R, Gandhi D, Case I, Teknos T, Mukherji SK. Can pretreatment CT perfusion predict response of advanced squamous cell carcinoma of the upper aerodigestive tract treated with induction chemotherapy? AJNR Am J Neuroradiol. 2007;28(2):328–34.PubMedGoogle Scholar
  24. 24.
    King AD, Vlantis AC, Tsang RK, Gary TM, Au AK, Chan CY, Kok SY, Kwok WT, Lui HK, Ahuja AT. Magnetic resonance imaging for the detection of nasopharyngeal carcinoma. AJNR Am J Neuroradiol. 2006;27(6):1288–91.PubMedGoogle Scholar
  25. 25.
    Guiu B, Petit JM, Capitan V, Aho S, Masson D, Lefevre PH, Favelier S, Loffroy R, Verges B, Hillon P, et al. Intravoxel incoherent motion diffusion-weighted imaging in nonalcoholic fatty liver disease: a 3.0-T MR study. Radiology. 2012;265(1):96–103.CrossRefPubMedGoogle Scholar
  26. 26.
    Kim S, Loevner L, Quon H, Sherman E, Weinstein G, Kilger A, Poptani H. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res Off J Am Assoc Cancer Res. 2009;15(3):986–94.CrossRefGoogle Scholar
  27. 27.
    Rheinheimer S, Stieltjes B, Schneider F, Simon D, Pahernik S, Kauczor HU, Hallscheidt P. Investigation of renal lesions by diffusion-weighted magnetic resonance imaging applying intravoxel incoherent motion-derived parameters–initial experience. Eur J Radiol. 2012;81(3):e310–6.CrossRefPubMedGoogle Scholar
  28. 28.
    Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z. Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? AJNR Am J Neuroradiol. 2009;30(4):681–8.CrossRefPubMedGoogle Scholar
  29. 29.
    Zonari P, Baraldi P, Crisi G. Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging. Neuroradiology. 2007;49(10):795–803.CrossRefPubMedGoogle Scholar
  30. 30.
    Maeda M, Kato H, Sakuma H, Maier SE, Takeda K. Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol. 2005;26(5):1186–92.PubMedGoogle Scholar
  31. 31.
    Martinez Barbero JP, Rodriquez Jimenez I, Martin Noguerol T, Luna Alcala A. Utility of MRI diffusion techniques in the evaluation of tumors of the head and neck. Cancers. 2013;5(3):875–89.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Healthcare 2016

Authors and Affiliations

  1. 1.Department of RadiologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina

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