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Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors

  • Head-Neck-ENT Radiology
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Abstract

Purpose

To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types.

Methods

In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin’s tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (Ktrans, Kep, and Ve) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal–Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses.

Results

PAs exhibited a lowest Ktrans among these four PTs. WTs demonstrated the highest Kep and lowest Ve values. WTs and MTs showed lower ADCmin values than PAs and OBNs. The combination of Kep and Ve provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADCmin cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. Ktrans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs.

Conclusion

The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

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References

  1. Upton DC, McNamar JP, Connor NP, Harari PM, Hartig GK (2007) Parotidectomy: ten-year review of 237 cases at a single institution. Otolaryngol Head Neck Surg 136(5):788–792

    Article  PubMed  Google Scholar 

  2. Freling N, Crippa F, Maroldi R (2016) Staging and follow-up of high-grade malignant salivary gland tumours: the role of traditional versus functional imaging approaches - a review. Oral Oncol 60:157–166

    Article  PubMed  Google Scholar 

  3. Witt RL (2002) The significance of the margin in parotid surgery for pleomorphic adenoma. Laryngoscope 112(12):2141–2154

    Article  PubMed  Google Scholar 

  4. Sciubba JJ, Brannon RB (1982) Myoepithelioma of salivary glands: report of 23 cases. Cancer 49(3):562–572

    Article  CAS  PubMed  Google Scholar 

  5. Bradley PT, Paleri V, Homer JJ (2012) Consensus statement by otolaryngologists on the diagnosis and management of benign parotid gland disease. Clin Otolaryngol 37(4):300–304

    Article  CAS  PubMed  Google Scholar 

  6. Zbaren P, Vander Poorten V, Witt RL, Woolgar JA, Shaha AR, Triantafyllou A, Takes RP, Rinaldo A, Ferlito A (2013) Pleomorphic adenoma of the parotid: formal parotidectomy or limited surgery? Am J Surg 205(1):109–118

    Article  PubMed  Google Scholar 

  7. Magnano M, Gervasio CF, Cravero L, Machetta G, Lerda W, Beltramo G, Orecchia R, Ragona R, Bussi M (1999) Treatment of malignant neoplasms of the parotid gland. Otolaryngol Head Neck Surg 121(5):627–632

    Article  CAS  PubMed  Google Scholar 

  8. Prades JM, Oletski A, Faye MB, Dumollard JM, Timoshenko AP, Veyret C, Peoc’h M, Martin C (2007) Parotid gland masses: diagnostic value of MR imaging with histopathologic correlations. Morphologie 91(292):44–51

    Article  PubMed  Google Scholar 

  9. Yousem DM, Kraut MA, Chalian AA (2000) Major salivary gland imaging. Radiology 216(1):19–29

    Article  CAS  PubMed  Google Scholar 

  10. Freling NJ, Molenaar WM, Vermey A, Mooyaart EL, Panders AK, Annyas AA, Thijn CJ (1992) Malignant parotid tumors: clinical use of MR imaging and histologic correlation. Radiology 185(3):691–696

    Article  CAS  PubMed  Google Scholar 

  11. Elmokadem AH, Abdel Khalek AM, Abdel Wahab RM, Tharwat N, Gaballa GM, Elata MA, Amer T (2019) Diagnostic accuracy of multiparametric magnetic resonance imaging for differentiation between parotid neoplasms. Can Assoc Radiol J 70(3):264–272

    Article  PubMed  Google Scholar 

  12. Yabuuchi H, Fukuya T, Tajima T, Hachitanda Y, Tomita K, Koga M (2003) Salivary gland tumors: diagnostic value of gadolinium-enhanced dynamic MR imaging with histopathologic correlation. Radiology 226(2):345–354

    Article  PubMed  Google Scholar 

  13. Kitamoto E, Chikui T, Kawano S, Ohga M, Kobayashi K, Matsuo Y, Yoshiura T, Obara M, Honda H, Yoshiura K (2015) The application of dynamic contrast-enhanced MRI and diffusion-weighted MRI in patients with maxillofacial tumors. Acad Radiol 22(2):210–216

    Article  PubMed  Google Scholar 

  14. Yabuuchi H, Matsuo Y, Kamitani T, Setoguchi T, Okafuji T, Soeda H, Sakai S, Hatakenaka M, Nakashima T, Oda Y et al (2008) Parotid gland tumors: can addition of diffusion-weighted MR imaging to dynamic contrast-enhanced MR imaging improve diagnostic accuracy in characterization? Radiology 249(3):909–916

    Article  PubMed  Google Scholar 

  15. Habermann CR, Arndt C, Graessner J, Diestel L, Petersen KU, Reitmeier F, Ussmueller JO, Adam G, Jaehne M (2009) Diffusion-weighted echo-planar MR imaging of primary parotid gland tumors: is a prediction of different histologic subtypes possible? AJNR Am J Neuroradiol 30(3):591–596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Coudert H, Mirafzal S, Dissard A, Boyer L, Montoriol PF (2021) Multiparametric magnetic resonance imaging of parotid tumors: a systematic review. Diagn Interv Imaging 102(3):121–130

  17. Chikui T, Obara M, Simonetti AW, Ohga M, Koga S, Kawano S, Matsuo Y, Kamintani T, Shiraishi T, Kitamoto E et al (2012) The principal of dynamic contrast enhanced MRI, the method of pharmacokinetic analysis, and its application in the head and neck region. Int J Dent 2012:480659

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sumi M, Van Cauteren M, Sumi T, Obara M, Ichikawa Y, Nakamura T (2012) Salivary gland tumors: use of intravoxel incoherent motion MR imaging for assessment of diffusion and perfusion for the differentiation of benign from malignant tumors. Radiology 263(3):770–777

    Article  PubMed  Google Scholar 

  19. Koyasu S, Iima M, Umeoka S, Morisawa N, Porter DA, Ito J, Le Bihan D, Togashi K (2014) The clinical utility of reduced-distortion readout-segmented echo-planar imaging in the head and neck region: initial experience. Eur Radiol 24(12):3088–3096

    Article  PubMed  Google Scholar 

  20. Porter DA, Heidemann RM (2009) High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magn Reson Med 62(2):468–475

    Article  PubMed  Google Scholar 

  21. Leach MO, Morgan B, Tofts PS, Buckley DL, Huang W, Horsfield MA, Chenevert TL, Collins DJ, Jackson A, Lomas D et al (2012) Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging. Eur Radiol 22(7):1451–1464

    Article  CAS  PubMed  Google Scholar 

  22. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10(3):223–232

    Article  CAS  PubMed  Google Scholar 

  23. Patella F, Franceschelli G, Petrillo M, Sansone M, Fusco R, Pesapane F, Pompili G, Ierardi AM, Saibene AM, Moneghini L et al (2018) A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: a pilot study. Future Oncol 14(28):2893–2903

    Article  CAS  PubMed  Google Scholar 

  24. Patella F, Sansone M, Franceschelli G, Tofanelli L, Petrillo M, Fusco M, Nicolino GM, Buccimazza G, Fusco R, Gopalakrishnan V et al (2020) Quantification of heterogeneity to classify benign parotid tumors: a feasibility study on most frequent histotypes. Future Oncol 16(12):763–778

    Article  CAS  PubMed  Google Scholar 

  25. Xu Z, Zheng S, Pan A, Cheng X, Gao M (2019) A multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination. Eur J Nucl Med Mol Imaging 46(11):2228–2234

    Article  CAS  PubMed  Google Scholar 

  26. Park JY, Shin HJ, Shin KC, Sung YS, Choi WJ, Chae EY, Cha JH, Kim HH (2015) Comparison of readout segmented echo planar imaging (EPI) and EPI with reduced field-of-VIew diffusion-weighted imaging at 3t in patients with breast cancer. J Magn Reson Imaging 42(6):1679–1688

    Article  PubMed  Google Scholar 

  27. Yeom KW, Holdsworth SJ, Van AT, Iv M, Skare S, Lober RM, Bammer R (2013) Comparison of readout-segmented echo-planar imaging (EPI) and single-shot EPI in clinical application of diffusion-weighted imaging of the pediatric brain. AJR Am J Roentgenol 200(5):W437-443

    Article  PubMed  Google Scholar 

  28. Xu X, Wang Y, Hu H, Su G, Liu H, Shi H, Wu F (2017) Readout-segmented echo-planar diffusion-weighted imaging in the assessment of orbital tumors: comparison with conventional single-shot echo-planar imaging in image quality and diagnostic performance. Acta Radiol 58(12):1457–1467

    Article  PubMed  Google Scholar 

  29. Zhao M, Liu Z, Sha Y, Wang S, Ye X, Pan Y, Wang S (2016) Readout-segmented echo-planar imaging in the evaluation of sinonasal lesions: a comprehensive comparison of image quality in single-shot echo-planar imaging. Magn Reson Imaging 34(2):166–172

    Article  PubMed  Google Scholar 

  30. Wisner DJ, Rogers N, Deshpande VS, Newitt DN, Laub GA, Porter DA, Kornak J, Joe BN, Hylton NM (2014) High-resolution diffusion-weighted imaging for the separation of benign from malignant BI-RADS 4/5 lesions found on breast MRI at 3T. J Magn Reson Imaging 40(3):674–681

    Article  PubMed  Google Scholar 

  31. Ma G, Zhu LN, Su GY, Hu H, Qian W, Bu SS, Xu XQ, Wu FY (2018) Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors. Eur Arch Otorhinolaryngol 275(8):2151–2157

    Article  PubMed  Google Scholar 

  32. Zhang Z, Song C, Zhang Y, Wen B, Zhu J, Cheng J (2019) Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging. Dentomaxillofac Radiol 48(7):20190100

    Article  PubMed  PubMed Central  Google Scholar 

  33. Alibek S, Zenk J, Bozzato A, Lell M, Grunewald M, Anders K, Rabe C, Iro H, Bautz W, Greess H (2007) The value of dynamic MRI studies in parotid tumors. Acad Radiol 14(6):701–710

    Article  PubMed  Google Scholar 

  34. Gaddikeri S, Hippe DS, Anzai Y (2016) Dynamic Contrast-Enhanced MRI in the Evaluation of Carotid Space Paraganglioma versus Schwannoma. J Neuroimaging 26(6):618–625

    Article  PubMed  Google Scholar 

  35. Lee FK, King AD, Ma BB, Yeung DK (2012) Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) for differential diagnosis in head and neck cancers. Eur J Radiol 81(4):784–788

    Article  PubMed  Google Scholar 

  36. Yu JY, Zhang D, Huang XL, Ma J, Yang C, Li XJ, Xiong H, Zhou B, Liao RK, Tang ZY (2020) Quantitative analysis of DCE-MRI and RESOLVE-DWI for differentiating nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia. J Med Syst 44(4):75

    Article  CAS  PubMed  Google Scholar 

  37. Woo SH, Choi DS, Kim JP, Park JJ, Joo YH, Chung PS, Kim BY, Ko YH, Jeong HS, Kim HJ (2013) Two-phase computed tomography study of warthin tumor of parotid gland: differentiation from other parotid gland tumors and its pathologic explanation. J Comput Assist Tomogr 37(4):518–524

    Article  PubMed  Google Scholar 

  38. Furuya M, Yonemitsu Y (2008) Cancer neovascularization and proinflammatory microenvironments. Curr Cancer Drug Targets 8(4):253–265

    Article  CAS  PubMed  Google Scholar 

  39. Jain RK (2005) Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science 307(5706):58–62

    Article  CAS  PubMed  Google Scholar 

  40. Triest WE, Fried MP, Stanievich JF (1983) Membranous basal cell adenoma of the hypopharynx. Arch Otolaryngol 109(11):774–777

    Article  CAS  PubMed  Google Scholar 

  41. Ahn SJ, An CS, Koom WS, Song HT, Suh JS (2011) Correlations of 3T DCE-MRI quantitative parameters with microvessel density in a human-colorectal-cancer xenograft mouse model. Korean J Radiol 12(6):722–730

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kim SH, Lee HS, Kang BJ, Song BJ, Kim HB, Lee H, Jin MS, Lee A (2016) Dynamic contrast-enhanced MRI perfusion parameters as imaging biomarkers of angiogenesis. PLoS One 11(12):e0168632

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Yabuuchi H, Kamitani T, Sagiyama K, Yamasaki Y, Hida T, Matsuura Y, Hino T, Murayama Y, Yasumatsu R, Yamamoto H (2020) Characterization of parotid gland tumors: added value of permeability MR imaging to DWI and DCE-MRI. Eur Radiol 30(12):6402–6412

    Article  PubMed  Google Scholar 

  44. Ikeda M, Motoori K, Hanazawa T, Nagai Y, Yamamoto S, Ueda T, Funatsu H, Ito H (2004) Warthin tumor of the parotid gland: diagnostic value of MR imaging with histopathologic correlation. AJNR Am J Neuroradiol 25(7):1256–1262

    PubMed  PubMed Central  Google Scholar 

  45. Lee JY, Kim HJ, Kim YK, Cha J, Kim ST (2019) Basal cell adenoma and myoepithelioma of the parotid gland: patterns of enhancement at two-phase CT in comparison with Warthin tumor. Diagn Interv Radiol 25(4):285–290

    Article  PubMed  PubMed Central  Google Scholar 

  46. Eida S, Sumi M, Sakihama N, Takahashi H, Nakamura T (2007) Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy. AJNR Am J Neuroradiol 28(1):116–121

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Celebi I, Mahmutoglu AS, Ucgul A, Ulusay SM, Basak T, Basak M (2013) Quantitative diffusion-weighted magnetic resonance imaging in the evaluation of parotid gland masses: a study with histopathological correlation. Clin Imaging 37(2):232–238

    Article  PubMed  Google Scholar 

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Funding

This study was supported by the Startup Fund for scientific research of Fujian Medical University (No. 2019QH1105).

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Author notes

  1. Nan Huang and Zebin Xiao contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People’s Republic of China

    Nan Huang, Yu Chen, Dejun She, Wei Guo, Xiefeng Yang, Qi Chen, Dairong Cao & Tanhui Chen

  2. Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Zebin Xiao

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  1. Nan Huang
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Huang, N., Xiao, Z., Chen, Y. et al. Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors. Neuroradiology 63, 1709–1719 (2021). https://doi.org/10.1007/s00234-021-02758-z

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