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Using 3 Tesla magnetic resonance imaging in the pre-operative evaluation of tongue carcinoma

Published online by Cambridge University Press:  07 July 2017

K F Moreno ,
R S Cornelius ,
F V Lucas ,
J Meinzen-Derr  and
Y J Patil
K F Moreno
Affiliation:
Department of Otolaryngology Head and Neck Surgery, University of Cincinnati Medical Center College of Medicine; Ohio, USA
R S Cornelius
Affiliation:
Department of Radiology, University of Cincinnati Medical Center College of Medicine; Ohio, USA
F V Lucas
Affiliation:
Department of Pathology, University of Cincinnati Medical Center College of Medicine; Ohio, USA
J Meinzen-Derr
Affiliation:
Department of Otolaryngology Head and Neck Surgery, University of Cincinnati Medical Center College of Medicine; Ohio, USA
Y J Patil*
Affiliation:
Department of Otolaryngology Head and Neck Surgery, University of Cincinnati Medical Center College of Medicine; Ohio, USA
*
Address for correspondence: Dr Y Patil, Department of Otolaryngology Head and Neck Surgery, University of Cincinnati College of Medicine, ML 0528 Cincinnati, Ohio, USA Fax: +1 513 558 4477 E-mail: yash.patil@uc.edu
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Abstract

Objective:

This study aimed to evaluate the role of 3 Tesla magnetic resonance imaging in predicting tongue tumour thickness via direct and reconstructed measures, and their correlations with corresponding histological measures, nodal metastasis and extracapsular spread.

Methods:

A prospective study was conducted of 25 patients with histologically proven squamous cell carcinoma of the tongue and pre-operative 3 Tesla magnetic resonance imaging from 2009 to 2012.

Results:

Correlations between 3 Tesla magnetic resonance imaging and histological measures of tongue tumour thickness were assessed using the Pearson correlation coefficient: r values were 0.84 (p < 0.0001) and 0.81 (p < 0.0001) for direct and reconstructed measurements, respectively. For magnetic resonance imaging, direct measures of tumour thickness (mean ± standard deviation, 18.2 ± 7.3 mm) did not significantly differ from the reconstructed measures (mean ± standard deviation, 17.9 ± 7.2 mm; r = 0.879). Moreover, 3 Tesla magnetic resonance imaging had 83 per cent sensitivity, 82 per cent specificity, 82 per cent accuracy and a 90 per cent negative predictive value for detecting cervical lymph node metastasis.

Conclusion:

In this cohort, 3 Tesla magnetic resonance imaging measures of tumour thickness correlated highly with the corresponding histological measures. Further, 3 Tesla magnetic resonance imaging was an effective method of detecting malignant adenopathy with extracapsular spread.

Keywords

Mouth NeoplasmsMagnetic Resonance ImagingTongue NeoplasmsHead and Neck NeoplasmsLymphadenopathyLymph Nodes
Type
Main Articles
Information
The Journal of Laryngology & Otology , Volume 131 , Issue 9 , September 2017 , pp. 793 - 800
Copyright
Copyright © JLO (1984) Limited 2017 

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References

1 Shah, JP, Patel, SG, Singh, B, Shah, JP. Jatin Shah's Head and Neck Surgery and Oncology. Philadelphia, PA: Elsevier/Mosby, 2012 Google Scholar
2What are the key statistics about oral cavity and oropharyngeal cancers? American Cancer Society. In: http://www.cancer.org/cancer/oralcavityandoropharyngealcancer/detailedguide/oral-cavity-and-oropharyngeal-cancer-key-statistics [30 June 2016]Google Scholar
3 Ding, ZX, Liang, BL, Shen, J, Xie, BK, Huang, SQ, Zhang, B. Magnetic resonance imaging diagnosis of cervical lymph node metastasis from lingual squamous cell carcinoma [in Chinese]. Ai Zheng 2005;24:199–203 Google ScholarPubMed
4 Shaw, RJ, Lowe, D, Woolgar, JA, Brown, JS, Vaughan, ED, Evans, C et al. Extracapsular spread in oral squamous cell carcinoma. Head Neck 2010;32:714–22Google Scholar
5 Lwin, CT, Hanlon, R, Lowe, D, Brown, JS, Woolgar, JA, Triantafyllou, A et al. Accuracy of MRI in prediction of tumour thickness and nodal stage in oral squamous cell carcinoma. Oral Oncol 2012;48:149–54CrossRefGoogle ScholarPubMed
6 Park, JO, Jung, SL, Joo, YH, Jung, CK, Cho, KJ, Kim, MS. Diagnostic accuracy of magnetic resonance imaging (MRI) in the assessment of tumor invasion depth in oral/oropharyngeal cancer. Oral Oncol 2011;47:381–6CrossRefGoogle ScholarPubMed
7 Bashir, U, Manzoor, MU, Majeed, Y, Khan, RU, Hassan, U, Murtaza, A et al. Reliability of MRI in measuring tongue tumour thickness: a 1.5 T study. J Ayub Med Coll Abbottabad 2011;23:101–4Google Scholar
8 Okura, M, Iida, S, Aikawa, T, Adachi, T, Yoshimura, N, Yamada, T et al. Tumor thickness and paralingual distance of coronal MR imaging predicts cervical node metastases in oral tongue carcinoma. AJNR Am J Neuroradiol 2008;29:45–50 Google Scholar
9 Preda, L, Chiesa, F, Calabrese, L, Latronico, A, Bruschini, R, Leon, ME et al. Relationship between histologic thickness of tongue carcinoma and thickness estimated from preoperative MRI. Eur Radiol 2006;16:2242–8CrossRefGoogle ScholarPubMed
10 Lam, P, Au-Yeung, KM, Cheng, PW, Wei, WI, Yuen, AP, Trendell-Smith, N et al. Correlating MRI and histologic tumor thickness in the assessment of oral tongue cancer. AJR Am J Roentgenol 2004;182:803–8Google Scholar
11 Frayne, R, Goodyear, BG, Dickhoff, P, Lauzon, ML, Sevick, RJ. Magnetic resonance imaging at 3.0 Tesla: challenges and advantages in clinical neurological imaging. Invest Radiol 2003;38:385–402 CrossRefGoogle ScholarPubMed
12 Lu, H, Nagae-Poetscher, LM, Golay, X, Lin, D, Pomper, M, van Zijl, PC. Routine clinical brain MRI sequences for use at 3.0 Tesla. J Magn Reson Imaging 2005;22:13–22 CrossRefGoogle ScholarPubMed
13 Kim, LJ, Lekovic, GP, White, WL, Karis, J. Preliminary experience with 3-Tesla MRI and Cushing's disease. Skull Base 2007;17:273–7CrossRefGoogle ScholarPubMed
14 Fomekong, E, Duprez, T, Docquier, MA, Ntsambi, G, Maiter, D, Raftopoulos, C. Intraoperative 3 T MRI for pituitary macroadenoma resection: initial experience in 73 consecutive patients. Clin Neurol Neurosurg 2014;126:143–9CrossRefGoogle Scholar
15 Ulrich, NH, Ahmadli, U, Woernle, CM, Alzarhani, YA, Bertalanffy, H, Kollias, SS. Diffusion tensor imaging for anatomical localization of cranial nerves and cranial nerve nuclei in pontine lesions: initial experiences with 3 T-MRI. J Clin Neurosci 2014;21:1924–7CrossRefGoogle Scholar
16 Qiu, LH, Zhang, JW, Li, SP, Xie, C, Yao, ZW, Feng, XY. Molecular imaging of angiogenesis to delineate the tumor margins in glioma rat model with endoglin-targeted paramagnetic liposomes using 3 T MRI. J Magn Reson Imaging 2015;41:1056–64Google Scholar
17 Watanabe, Y, Yamasaki, F, Kajiwara, Y, Takayasu, T, Nosaka, R, Akiyama, Y et al. Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3 T MRI. Eur J Radiol 2013;82:658–63CrossRefGoogle Scholar
18 Patel, U, Dasgupta, P, Challacombe, B, Cahill, D, Brown, C, Patel, R et al. Pre-biopsy 3 T MRI and targeted biopsy of the index prostate cancer - correlation with robot assisted radical prostatectomy. BJU Int 2017;119:82–90 Google Scholar
19 Styles, C, Ferris, N, Mitchell, C, Murphy, D, Frydenberg, M, Mills, J et al. Multiparametric 3 T MRI in the evaluation of intraglandular prostate cancer: correlation with histopathology. J Med Imaging Radiat Oncol 2014;58:439–48CrossRefGoogle Scholar
20 Largeron, JP, Galonnier, F, Vedrine, N, Alfidja, A, Boyer, L, Pereira, B et al. Multiparametric 3 T MRI in the routine staging of prostate cancer [in French]. Prog Urol 2014;24:145–53CrossRefGoogle Scholar
21 Ferda, J, Kastner, J, Hora, M, Hes, O, Finek, J, Topolcan, O et al. A role of multifactorial evaluation of prostatic 3 T MRI in patients with elevated prostatic-specific antigen levels: prospective comparison with ultrasound-guided transrectal biopsy. Anticancer Res 2013;33:2791–5Google Scholar
22 Srinivasan, A, Dvorak, R, Rohrer, S, Mukherji, SK. Initial experience of 3-tesla apparent diffusion coefficient values in characterizing squamous cell carcinomas of the head and neck. Acta Radiol 2008;49:1079–84CrossRefGoogle ScholarPubMed
23 Lee, MC, Tsai, HY, Chuang, KS, Liu, CK, Chen, MK. Prediction of nodal metastasis in head and neck cancer using a 3 T MRI ADC map. AJNR Am J Neuroradiol 2013;34:864–9Google Scholar
24 Iwai, H, Kyomoto, R, Ha-Kawa, SK, Lee, S, Yamashita, T. Magnetic resonance determination of tumor thickness as predictive factor of cervical metastasis in oral tongue carcinoma. Laryngoscope 2002;112:457–61CrossRefGoogle ScholarPubMed
25The Oral Cancer Foundation. In: http://oralcancerfoundation.org/ [20 October 2016]Google Scholar
26 Stehling, C, Vieth, V, Bachmann, R, Nassenstein, I, Kugel, H, Kooijman, H et al. High-resolution magnetic resonance imaging of the temporomandibular joint: image quality at 1.5 and 3.0 Tesla in volunteers. Invest Radiol 2007;42:428–34Google Scholar
27 Yesuratnam, A, Wiesenfeld, D, Tsui, A, Iseli, TA, Hoorn, SV, Ang, MT et al. Preoperative evaluation of oral tongue squamous cell carcinoma with intraoral ultrasound and magnetic resonance imaging-comparison with histopathological tumour thickness and accuracy in guiding patient management. Int J Oral Maxillofac Surg 2014;43:787–94CrossRefGoogle ScholarPubMed
28 Zeng, H, Liang, CH, Zhou, ZG, Zheng, JH, Zeng, QX. Study of preoperative MRI staging of tongue carcinoma in relation to pathological findings [in Chinese]. Di Yi Jun Yi Da Xue Xue Bao 2003;23:841–3Google Scholar
29 Kwon, M, Moon, H, Nam, SY, Lee, JH, Kim, JW, Lee, YS et al. Clinical significance of three-dimensional measurement of tumour thickness on magnetic resonance imaging in patients with oral tongue squamous cell carcinoma. Eur Radiol 2016;26:858–65CrossRefGoogle ScholarPubMed