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European Radiology

, Volume 28, Issue 9, pp 3861–3871 | Cite as

Facial nerve tractography: A new tool for the detection of perineural spread in parotid cancers

  • René-Charles Rouchy
  • Arnaud Attyé
  • Maud Medici
  • Félix Renard
  • Adrian Kastler
  • Sylvie Grand
  • Irène Tropres
  • Christian Adrien Righini
  • Alexandre Krainik
Head and Neck
  • 123 Downloads

Abstract

Objectives

To determine whether facial nerve MR tractography is useful in detecting PeriNeural Spread in parotid cancers.

Methods

Forty-five participants were enrolled. Thirty patients with surgically managed parotid tumors (15 malignant, 15 benign) were compared with 15 healthy volunteers. All of them had undergone 3T-MRI with diffusion acquisition and post-processing constrained spherical deconvolution-based tractography. Parameters of diffusion-weighted sequences were b-value 1,000 s/mm2, 32 directions. Two radiologists performed a blinded visual reading of tractographic maps and graded the facial nerve average pathlength and fractional anisotropy (FA). We also compared diagnostic accuracy of tractography with morphological MRI sequences to detect PeriNeural Spread. Non-parametric methods were used.

Results

Average pathlength was significantly higher in cases with PeriNeural Spread (39.86 mm [Quartile1: 36.27; Quartile3: 51.19]) versus cases without (16.23 mm [12.90; 24.90]), p<0.001. The threshold above which there was a significant association with PeriNeural Spread was set at 27.36 mm (Se: 100%; Sp: 84%; AUC: 0.96, 95% CI 0.904–1). There were no significant differences in FA between groups. Tractography map visual analyses directly displayed PeriNeural Spread in distal neural ramifications with sensitivity of 75%, versus 50% using morphological sequences.

Conclusions

Tractography could be used to identify facial nerve PeriNeural Spread by parotid cancers.

Key Points

• Tractography could detect facial nerve PeriNeural Spread in parotid cancers.

• The average pathlength parameter is increased in case of PeriNeural Spread.

• Tractography could map PeriNeural Spread more precisely than conventional imaging.

Keywords

Facial nerve PeriNeural Spread Parotid cancers Tractography Track-weighted imaging 

Abbreviations

AP

Average pathlength

CF

Cervicofacial

CSD

Constrained spherical deconvolution

FA

Fractional anisotropy

PFP

Peripheral facial palsy

PNS

PeriNeural Spread

TF

Temporofacial

TWI

Track-weighted imaging

VIIn

Facial nerve

Notes

Acknowledgements

The authors acknowledge the valuable assistance of Patrice Jousse for his work editing the MRI images and diagrams. We also thank Dr Louise Ball and Dr Jeanne Maurice for critically editing the manuscript.

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Prof. Alexandre Krainik.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (MM) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained (IRB 5891 for patients/ IRB 6705 for healthy volunteers).

Methodology

prospective

diagnostic study

performed at one institution

Supplementary material

330_2018_5318_MOESM1_ESM.mp4 (12.8 mb)
ESM 1 (MP4 13106 kb)

References

  1. 1.
    Liebig C, Ayala G, Wilks JA et al (2009) Perineural invasion in cancer: a review of the literature. Cancer 115:3379–3391CrossRefPubMedGoogle Scholar
  2. 2.
    Johnston M, Yu E, Kim J (2012) Perineural invasion and spread in head and neck cancer. Expert Rev Anticancer Ther 12:359–371CrossRefPubMedGoogle Scholar
  3. 3.
    Roh J, Muelleman T, Tawfik O, Thomas SM (2015) Perineural growth in head and neck squamous cell carcinoma: a review. Oral Oncol 51:16–23CrossRefPubMedGoogle Scholar
  4. 4.
    Barrett AW, Speight PM (2009) Perineural invasion in adenoid cystic carcinoma of the salivary glands: a valid prognostic indicator? Oral Oncol 45:936–940CrossRefPubMedGoogle Scholar
  5. 5.
    Jegadeesh N, Liu Y, Prabhu RS et al (2015) Outcomes and prognostic factors in modern era management of major salivary gland cancer. Oral Oncol 51:770–777CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Andry G, Hamoir M, Locati LD et al (2012) Management of salivary gland tumors. Expert Rev Anticancer Ther 12:1161–1168CrossRefPubMedGoogle Scholar
  7. 7.
    Chen AM, Garcia J, Granchi P et al (2009) Base of skull recurrences after treatment of salivary gland cancer with perineural invasion reduced by postoperative radiotherapy. Clin Otolaryngol Off J ENT-UK Off J Neth Soc Oto-Rhino-Laryngol Cervico-Facial Surg 34:539–545Google Scholar
  8. 8.
    Yabuuchi H, Fukuya T, Tajima T et al (2003) Salivary gland tumors: diagnostic value of gadolinium-enhanced dynamic MR imaging with histopathologic correlation. Radiology 226:345–354CrossRefPubMedGoogle Scholar
  9. 9.
    Yabuuchi H, Matsuo Y, Kamitani T 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 249:909–916CrossRefPubMedGoogle Scholar
  10. 10.
    Caldemeyer KS, Mathews VP, Righi PD, Smith RR (1998) Imaging features and clinical significance of perineural spread or extension of head and neck tumors. Radiogr Rev Publ Radiol Soc N Am Inc 18:97–110 quiz 147Google Scholar
  11. 11.
    Nemzek WR, Hecht S, Gandour-Edwards R et al (1998) Perineural spread of head and neck tumors: how accurate is MR imaging? AJNR Am J Neuroradiol 19:701–706PubMedGoogle Scholar
  12. 12.
    Li X, Chen J, Hong G et al (2013) In vivo DTI longitudinal measurements of acute sciatic nerve traction injury and the association with pathological and functional changes. Eur J Radiol 82:e707–e714CrossRefPubMedGoogle Scholar
  13. 13.
    Takagi T, Nakamura M, Yamada M et al (2009) Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography. NeuroImage 44:884–892CrossRefPubMedGoogle Scholar
  14. 14.
    Breckwoldt MO, Stock C, Xia A et al (2015) Diffusion Tensor Imaging Adds Diagnostic Accuracy in Magnetic Resonance Neurography. Invest Radiol 50:498–504CrossRefPubMedGoogle Scholar
  15. 15.
    Basser PJ (1995) Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:333–344CrossRefPubMedGoogle Scholar
  16. 16.
    Attyé A, Troprès I, Rouchy R-C et al (2016) Diffusion MRI: literature review in salivary gland tumors. Oral Dis.  https://doi.org/10.1111/odi.12543
  17. 17.
    Attyé A, Karkas A, Troprés I et al (2016) Parotid gland tumours: MRtractography to assess contact with the facial nerve. Eur Radiol 26(7):2233–2241Google Scholar
  18. 18.
    Tournier J-D, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35:1459–1472CrossRefPubMedGoogle Scholar
  19. 19.
    Calamante F, Tournier J-D, Kurniawan ND et al (2012) Super-resolution track-density imaging studies of mouse brain: comparison to histology. NeuroImage 59:286–296CrossRefPubMedGoogle Scholar
  20. 20.
    Calamante F (2016) Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification. AJNR Am J Neuroradiol 37(6):1066–1067Google Scholar
  21. 21.
    Calamante F (2017) Track-weighted imaging methods: extracting information from a streamlines tractogram. Magma N Y N.  https://doi.org/10.1007/s10334-017-0608-1
  22. 22.
    Andersson JLR, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20:870–888CrossRefPubMedGoogle Scholar
  23. 23.
    Pannek K, Mathias JL, Bigler ED et al (2011) The average pathlength map: a diffusion MRI tractography-derived index for studying brain pathology. NeuroImage 55:133–141CrossRefPubMedGoogle Scholar
  24. 24.
    Calamante F, Tournier J-D, Heidemann RM et al (2011) Track density imaging (TDI): validation of super resolution property. NeuroImage 56:1259–1266CrossRefPubMedGoogle Scholar
  25. 25.
    Brown IS (2016) Pathology of Perineural Spread. J Neurol Surg Part B Skull Base 77:124–130CrossRefGoogle Scholar
  26. 26.
    Paes FM, Singer AD, Checkver AN et al (2013) Perineural spread in head and neck malignancies: clinical significance and evaluation with 18F-FDG PET/CT. Radiogr Rev Publ Radiol Soc N Am Inc 33:1717–1736Google Scholar
  27. 27.
    (2008) Atlas of Human Anatomy, 4th ed. Radiology 248:391–391. doi:  https://doi.org/10.1148/radiol.2482082518
  28. 28.
    Kwak HH, Park HD, Youn KH et al (2004) Branching patterns of the facial nerve and its communication with the auriculotemporal nerve. Surg Radiol Anat SRA 26:494–500CrossRefPubMedGoogle Scholar
  29. 29.
    Tournier J-D, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23:1176–1185CrossRefPubMedGoogle Scholar
  30. 30.
    Farquharson S, Tournier J-D, Calamante F et al (2013) White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 118:1367–1377CrossRefPubMedGoogle Scholar
  31. 31.
    Barajas RF, Hess CP, Phillips JJ et al (2013) Super-resolution track density imaging of glioblastoma: histopathologic correlation. AJNR Am J Neuroradiol 34:1319–1325CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Jones DK, Knösche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 73:239–254CrossRefPubMedGoogle Scholar
  33. 33.
    Farquharson S, Tournier J-D, Calamante F et al (2016) Periventricular nodular heterotopia: detection of abnormal microanatomic fiber structures with whole-brain diffusion mr imaging tractography. Radiology 150852. doi:  https://doi.org/10.1148/radiol.2016150852
  34. 34.
    Lim JC, Phal PM, Desmond PM et al (2015) Probabilistic MRI tractography of the optic radiation using constrained spherical deconvolution: a feasibility study. PloS One 10:e0118948CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Reijmer YD, Leemans A, Heringa SM et al (2012) Improved sensitivity to cerebral white matter abnormalities in Alzheimer’s disease with spherical deconvolution based tractography. PloS One 7:e44074CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • René-Charles Rouchy
    • 1
    • 2
  • Arnaud Attyé
    • 1
    • 2
  • Maud Medici
    • 3
    • 4
  • Félix Renard
    • 2
  • Adrian Kastler
    • 1
    • 2
  • Sylvie Grand
    • 1
    • 2
  • Irène Tropres
    • 2
    • 5
  • Christian Adrien Righini
    • 6
  • Alexandre Krainik
    • 1
    • 2
  1. 1.Department of Neuroradiology and MRIGrenoble Alpes University Hospital – SFR RMN NeurosciencesGrenobleFrance
  2. 2.University of Grenoble Alpes, IRMaGeGrenobleFrance
  3. 3.Clinical Investigation Centre 1406 - Innovative TechnologyNational Institute of Health and Medical ResearchGrenobleFrance
  4. 4.Public Health DepartmentGrenoble Alpes University HospitalGrenobleFrance
  5. 5.IRMaGe, Inserm US 17, CNRS UMS 3552GrenobleFrance
  6. 6.Department of OtololaryngologyGrenoble Alpes University HospitalGrenobleFrance

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