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Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters—preliminary results

  • Head and Neck Radiology
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An EDITORIAL to this article was published on 21 April 2013

Abstract

Introduction

Using the intravoxel incoherent motion (IVIM) model, diffusion-related coefficient (D) and perfusion-related parameter (f) can be measured. Here, we used IVIM imaging to characterize squamous cell carcinomas of head and neck (HNSCC) and evaluated its application in follow-up after nonsurgical organ preserving therapy.

Methods

Twenty-two patients with locally advanced HNSCC (clinical stage III to IVb) were examined before treatment using eight different b values (b = 0, 50, 100, 150, 200, 250, 700, 800 s/mm2). All patients were followed for at least 7.5 months after conclusion of therapy. In 16 of these patients, follow-up MRI was available. Using the IVIM approach, f and D were extracted using a bi-exponential fit. For comparison, ADC maps were calculated.

Results

The initial values of f before therapy were located between 5.9 % and 12.9 % (mean: 9.4 ± 2.4 %) except for two outliers (f = 17.9 % and 18.2 %). These two patients exclusively displayed poor initial treatment response. Overall, high initial f (13.1 ± 4.1 % vs. 9.1 ± 2.4 %) and ADC (1.17 ± 0.08 × 10−3 mm2/s vs. 0.98 ± 0.19 × 10−3 mm2/s) were associated with poor short term outcome (n = 6) after 7.5 months follow-up. D values before treatment were 0.98 × 10−3 ± 0.18 mm2/s and ADC values were 1.03 × 10−3 ± 0.18 mm2/s. At follow-up, in all primary responders, D (69 ± 52 %), f (65 ± 46 %), and ADC (68 ± 49%) increased.

Conclusions

Our preliminary evaluation indicates that an initial high f may predict poor prognosis in HNSCC. In responders, a significant increase of all IVIM parameters after therapy was demonstrated.

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References

  1. Pfister DG, Su YB, Kraus DH, Wolden SL, Lis E, Aliff TB, Zahalsky AJ, Lake S, Needle MN, Shaha AR, Shah JP, Zelefsky MJ (2006) Concurrent cetuximab, cisplatin, and concomitant boost radiotherapy for locoregionally advanced, squamous cell head and neck cancer: a pilot phase II study of a new combined-modality paradigm. J Clin Oncol 24(7):1072–1078

    Article  PubMed  CAS  Google Scholar 

  2. Bonner JA, Harari PM, Giralt J, Cohen RB, Jones CU, Sur RK, Raben D, Baselga J, Spencer SA, Zhu J, Youssoufian H, Rowinsky EK, Ang KK (2010) Radiotherapy plus cetuximab for locoregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomised trial, and relation between cetuximab-induced rash and survival. Lancet Oncol 11(1):21–28

    Article  PubMed  CAS  Google Scholar 

  3. Chenevert TL, Meyer CR, Moffat BA, Rehemtulla A, Mukherji SK, Gebarski SS, Quint DJ, Robertson PL, Lawrence TS, Junck L, Taylor JM, Johnson TD, Dong Q, Muraszko KM, Brunberg JA, Ross BD (2002) Diffusion MRI: a new strategy for assessment of cancer therapeutic efficacy. Mol Imaging 1(4):336–343

    Article  PubMed  Google Scholar 

  4. Wang J, Takashima S, Takayama F, Kawakami S, Saito A, Matsushita T, Momose M, Ishiyama T (2001) Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 220(3):621–630

    Article  PubMed  CAS  Google Scholar 

  5. Srinivasan A, Dvorak R, Perni K, Rohrer S, Mukherji SK (2008) Differentiation of benign and malignant pathology in the head and neck using 3T apparent diffusion coefficient values: early experience. AJNR Am J Neuroradiol 29(1):40–44

    Article  PubMed  CAS  Google Scholar 

  6. Hatakenaka M, Nakamura K, Yabuuchi H, Shioyama Y, Matsuo Y, Ohnishi K, Sunami S, Kamitani T, Setoguchi T, Yoshiura T, Nakashima T, Nishikawa K, Honda H (2011) Pretreatment apparent diffusion coefficient of the primary lesion correlates with local failure in head-and-neck cancer treated with chemoradiotherapy or radiotherapy. Int J Radiat Oncol Biol Phys 81(2):339–345

    Article  PubMed  Google Scholar 

  7. Kim S, Loevner L, Quon H, Sherman E, Weinstein G, Kilger A, Poptani H (2009) 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 15(3):986–994

    Article  CAS  Google Scholar 

  8. Kato H, Kanematsu M, Tanaka O, Mizuta K, Aoki M, Shibata T, Yamashita T, Hirose Y, Hoshi H (2009) Head and neck squamous cell carcinoma: usefulness of diffusion-weighted MR imaging in the prediction of a neoadjuvant therapeutic effect. Eur Radiol 19(1):103–109

    Article  PubMed  Google Scholar 

  9. Vandecaveye V, Dirix P, De Keyzer F, Op de Beeck K, Vander Poorten V, Hauben E, Lambrecht M, Nuyts S, Hermans R (2012) Diffusion-weighted magnetic resonance imaging early after chemoradiotherapy to monitor treatment response in head-and-neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 82(3):1098–1107

    Article  PubMed  Google Scholar 

  10. King AD, Mo FK, Yu KH, Yeung DK, Zhou H, Bhatia KS, Tse GM, Vlantis AC, Wong JK, Ahuja AT (2010) Squamous cell carcinoma of the head and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response. Eur Radiol 20(9):2213–2220

    Article  PubMed  Google Scholar 

  11. Vandecaveye V, Dirix P, De Keyzer F, de Beeck KO, Vander Poorten V, Roebben I, Nuyts S, Hermans R (2010) Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for head and neck squamous cell carcinoma. Eur Radiol 20(7):1703–1714

    Article  PubMed  Google Scholar 

  12. Le Bihan D (2008) Intravoxel incoherent motion perfusion MR imaging: a wake-up call. Radiology 249(3):748–752

    Article  PubMed  Google Scholar 

  13. Le Bihan D (1988) Intravoxel incoherent motion imaging using steady-state free precession. Magn Reson Med 7(3):346–351

    Article  PubMed  Google Scholar 

  14. Lemke A, Laun FB, Simon D, Stieltjes B, Schad LR (2010) An in vivo verification of the intravoxel incoherent motion effect in diffusion-weighted imaging of the abdomen. Magn Reson Med 64(6):1580–1585

    Article  PubMed  Google Scholar 

  15. Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S, Schad LR, Stieltjes B (2009) Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Invest Radiol 44(12):769–775

    Article  PubMed  Google Scholar 

  16. Sumi M, Nakamura T (2012) Head and Neck tumors: assessment of perfusion-related parameters and diffusion coefficients based on the intravoxel incoherent motion model. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A3227

  17. Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, Sodickson DK, Goldberg JD, Formenti S, Moy L (2011) Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn Reson Med 65(5):1437–1447

    Article  PubMed  CAS  Google Scholar 

  18. Chandarana H, Kang SK, Wong S, Rusinek H, Zhang JL, Arizono S, Huang WC, Melamed J, Babb JS, Suan EF, Lee VS, Sigmund EE (2012) Diffusion-weighted intravoxel incoherent motion imaging of renal tumors with histopathologic correlation. Invest Radiol 47(12):688–696

    Article  PubMed  Google Scholar 

  19. Dopfert J, Lemke A, Weidner A, Schad LR (2011) Investigation of prostate cancer using diffusion-weighted intravoxel incoherent motion imaging. Magn Reson Imaging 29(8):1053–1058

    Article  PubMed  Google Scholar 

  20. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247

    Article  PubMed  CAS  Google Scholar 

  21. Cao Y, Popovtzer A, Li D, Chepeha DB, Moyer JS, Prince ME, Worden F, Teknos T, Bradford C, Mukherji SK, Eisbruch A (2008) 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 72(5):1287–1290

    Article  PubMed  Google Scholar 

  22. Chawla S, Kim S, Dougherty L, Wang S, Loevner LA, Quon H, Poptani H (2013) 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 200(1):35–43

    Article  PubMed  Google Scholar 

  23. Zima A, Carlos R, Gandhi D, Case I, Teknos T, Mukherji SK (2007) Can pretreatment CT perfusion predict response of advanced squamous cell carcinoma of the upper aerodigestive tract treated with induction chemotherapy? AJNR Am J Neuroradiol 28(2):328–334

    PubMed  CAS  Google Scholar 

  24. Surlan-Popovic K, Bisdas S, Rumboldt Z, Koh TS, Strojan P (2010) Changes in perfusion CT of advanced squamous cell carcinoma of the head and neck treated during the course of concomitant chemoradiotherapy. AJNR Am J Neuroradiol 31(3):570–575

    Article  PubMed  CAS  Google Scholar 

  25. Rheinheimer S, Stieltjes B, Schneider F, Simon D, Pahernik S, Kauczor HU, Hallscheidt P (2011) Investigation of renal lesions by diffusion-weighted magnetic resonance imaging applying intravoxel incoherent motion-derived parameters-Initial experience. Eur J Radiol 81(3):310–316

    Google Scholar 

  26. Arvinda HR, Kesavadas C, Sarma PS, Thomas B, Radhakrishnan VV, Gupta AK, Kapilamoorthy TR, Nair S (2009) Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 94(1):87–96

    Article  PubMed  CAS  Google Scholar 

  27. Morita N, Wang S, Chawla S, Poptani H, Melhem ER (2010) Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas. J Magn Reson Imaging JMRI 32(4):803–808

    Article  Google Scholar 

  28. Pickles MD, Manton DJ, Lowry M, Turnbull LW (2009) Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy. Eur J Radiol 71(3):498–505

    Article  PubMed  Google Scholar 

  29. Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z (2009) 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 30(4):681–688

    Article  PubMed  CAS  Google Scholar 

  30. Razek AA, Megahed AS, Denewer A, Motamed A, Tawfik A, Nada N (2008) Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors. Acta Radiol 49(3):364–370

    Article  PubMed  Google Scholar 

  31. Friedrich KM, Matzek W, Gentzsch S, Sulzbacher I, Czerny C, Herneth AM (2008) Diffusion-weighted magnetic resonance imaging of head and neck squamous cell carcinomas. Eur J Radiol 68(3):493–498

    Article  PubMed  Google Scholar 

  32. Kajiyama K, Maeda T, Takenaka K, Sugimachi K, Tsuneyoshi M (1999) The significance of stromal desmoplasia in intrahepatic cholangiocarcinoma: a special reference of ‘scirrhous-type’ and ‘nonscirrhous-type’ growth. Am J Surg Pathol 23(8):892–902

    Article  PubMed  CAS  Google Scholar 

  33. 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(9):773–784

    Article  PubMed  Google Scholar 

  34. Brizel DM, Dodge RK, Clough RW, Dewhirst MW (1999) Oxygenation of head and neck cancer: changes during radiotherapy and impact on treatment outcome. Radiother Oncol 53(2):113–117

    Article  PubMed  CAS  Google Scholar 

  35. Tatum JL, Kelloff GJ, Gillies RJ, Arbeit JM, Brown JM, Chao KS, Chapman JD, Eckelman WC, Fyles AW, Giaccia AJ, Hill RP, Koch CJ, Krishna MC, Krohn KA, Lewis JS, Mason RP, Melillo G, Padhani AR, Powis G, Rajendran JG, Reba R, Robinson SP, Semenza GL, Swartz HM, Vaupel P, Yang D, Croft B, Hoffman J, Liu G, Stone H, Sullivan D (2006) Hypoxia: importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol 82(10):699–757

    Article  PubMed  CAS  Google Scholar 

  36. Moffat BA, Chenevert TL, Meyer CR, McKeever PE, Hall DE, Hoff BA, Johnson TD, Rehemtulla A, Ross BD (2006) The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. Neoplasia 8(4):259–267

    Article  PubMed  CAS  Google Scholar 

  37. Seierstad T, Roe K, Olsen DR (2007) Noninvasive monitoring of radiation-induced treatment response using proton magnetic resonance spectroscopy and diffusion-weighted magnetic resonance imaging in a colorectal tumor model. Radiother Oncol 85(2):187–194

    Article  PubMed  Google Scholar 

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Hauser, T., Essig, M., Jensen, A. et al. Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters—preliminary results. Neuroradiology 55, 527–536 (2013). https://doi.org/10.1007/s00234-013-1154-9

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