Prediction of malignancy of submandibular gland tumors with apparent diffusion coefficient



This study was performed to predict malignancy of submandibular gland tumors using the apparent diffusion coefficient (ADC).


In total, 31 patients (19 male, 12 female; age, 16–71 years) with solid submandibular gland tumors were retrospectively analyzed. All patients underwent single-shot echo-planar diffusion-weighted magnetic resonance imaging of the submandibular gland region. ADC maps of the submandibular gland were reconstructed. The ADC value of the submandibular gland tumors was calculated. A freehand region of interest encompassing the homogenous tumor and solid part of the heterogeneous tumor was established.


The mean ADC for submandibular gland malignancy (1.15 ± 0.09 × 10−3 mm2/s) was significantly lower than that for benignancy (1.55 ± 0.25 × 10−3 mm2/s, P = 0.001). An ADC of 1.26 × 10−3 mm2/s could predict malignancy of submandibular gland tumors with an area under the curve of 0.869, accuracy of 84%, sensitivity of 88%, and specificity of 81%.


The ADC is a noninvasive imaging parameter that can be used for prediction of malignancy of submandibular gland tumors.

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  1. 1.

    Atula T, Panigrahi J, Tarkkanen J, Mäkitie A, Aro K. Preoperative evaluation and surgical planning of submandibular gland tumors. Head Neck. 2017;39:1071–7.

    Article  PubMed  Google Scholar 

  2. 2.

    Mizrachi A, Bachar G, Unger Y, Hilly O, Fliss DM, Shpitzer T. Submandibular salivary gland tumors: clinical course and outcome of a 20-year multicenter study. Ear Nose Throat J. 2017;96:E17–E20.

    Article  PubMed  Google Scholar 

  3. 3.

    Lee RJ, Tan AP, Tong EL, Satyadev N, Christensen RE. Epidemiology, prognostic factors, and treatment of malignant submandibular gland tumors: a population-based cohort analysis. JAMA Otolaryngol Head Neck Surg. 2015;141:905–12.

    Article  PubMed  Google Scholar 

  4. 4.

    Dalgic A, Karakoc O, Karahatay S, Hidir Y, Gamsizkan M, Birkent H, et al. Submandibular triangle masses. J Craniofac Surg. 2013;24:e529–e31.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Ziglinas P, Arnold A, Arnold M, Zbären P. Primary tumors of the submandibular glands: a retrospective study based on 41 cases. Oral Oncol. 2010;46:287–91.

    Article  PubMed  Google Scholar 

  6. 6.

    Becerril-Ramírez PB, Bravo-Escobar GA, Prado-Calleros HM, Castillo-Ventura BB, Pombo-Nava A. Histology of submandibular gland tumours, 10 years’ experience. Acta Otorrinolaringol Esp. 2011;62:432–5.

    Article  PubMed  Google Scholar 

  7. 7.

    Agarwal AK, Kanekar SG. Submandibular and sublingual spaces: diagnostic imaging and evaluation. Otolaryngol Clin North Am. 2012;45:1311–23.

    Article  PubMed  Google Scholar 

  8. 8.

    Knopf A, Cortolezis N, Bas M, Mansour N, Hofauer B. Multimodal ultrasonographic algorithm in the differentiation of submandibular masses. Acta Otolaryngol. 2017;137:640–5.

    Article  PubMed  Google Scholar 

  9. 9.

    Strieth S, Siedek V, Rytvina M, Gürkov R, Berghaus A, Clevert DA. Dynamic contrast-enhanced ultrasound for differential diagnosis of submandibular gland disease. Eur Arch Otorhinolaryngol. 2014;271:163–9.

    Article  PubMed  Google Scholar 

  10. 10.

    Abdel Razek AA, Ashmalla GA, Gaballa G, Nada N. Pilot study of ultrasound parotid imaging reporting and data system [PIRADS]: inter-observer agreement. Eur J Radiol. 2015;85:2533–8.

    Article  Google Scholar 

  11. 11.

    Kashiwagi N, Murakami T, Nakanishi K, Maenishi O, Okajima K, Takahashi H, et al. Conventional MRI findings for predicting submandibular pleomorphic adenoma. Acta Radiol. 2013;54:511–5.

    Article  PubMed  Google Scholar 

  12. 12.

    Abdel Razek AA, Samir S, Ashmalla GA. Characterization of parotid tumors with dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging and diffusion-weighted MR imaging. J Comput Assist Tomogr. 2017;41:131–6.

    Article  PubMed  Google Scholar 

  13. 13.

    Lam PD, Kuribayashi A, Imaizumi A, Sakamoto J, Sumi Y, Yoshino N, et al. Differentiating benign and malignant salivary gland tumours: diagnostic criteria and the accuracy of dynamic contrast-enhanced MRI with high temporal resolution. Br J Radiol. 2015;88:20140685.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Taylor MJ, Serpell JW, Thomson P. Preoperative fine needle cytology and imaging facilitates the management of submandibular salivary gland lesions. ANZ J Surg. 2011;81:70–4.

    Article  PubMed  Google Scholar 

  15. 15.

    Razek AA. Diffusion-weighted magnetic resonance imaging of head and neck. J Comput Assist Tomogr. 2010;34:808–15.

    Article  PubMed  Google Scholar 

  16. 16.

    Abdel Razek AA, Nada N. Role of diffusion-weighted MRI in differentiation of masticator space malignancy from infection. Dentomaxillofac Radiol. 2013;42:20120183.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Abdel Razek A, Mossad A, Ghonim M. Role of diffusion-weighted MR imaging in assessing malignant versus benign skull-base lesions. Radiol Med. 2011;116:125–32.

    Article  PubMed  Google Scholar 

  18. 18.

    Abdel Razek AA, Kamal E. Nasopharyngeal carcinoma: correlation of apparent diffusion coefficient value with prognostic parameters. Radiol Med. 2013;118:534–9.

    Article  PubMed  Google Scholar 

  19. 19.

    Razek AA, Sieza S, Maha B. Assessment of nasal and paranasal sinus masses by diffusion-weighted MR imaging. J Neuroradiol. 2009;36:206–11.

    Article  PubMed  Google Scholar 

  20. 20.

    Terra GT, Oliveira JX, Hernandez A, Lourenço SV, Arita ES, Cortes AR. Diffusion-weighted MRI for differentiation between sialadenitis and pleomorphic adenoma. Dentomaxillofac Radiol. 2017;46:20160257.

    Article  PubMed  Google Scholar 

  21. 21.

    Assili S, Fathi Kazerooni A, Aghaghazvini L, Saligheh Rad HR, Pirayesh Islamian J. Dynamic contrast magnetic resonance imaging [DCE-MRI] and diffusion weighted MR imaging [DWI] for differentiation between benign and malignant salivary gland tumors. J Biomed Phys Eng. 2015;5:157–68.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Tao X, Yang G, Wang P, Wu Y, Zhu W, Shi H, et al. The value of combining conventional, diffusion-weighted and dynamic contrast-enhanced MR imaging for the diagnosis of parotid gland tumours. Dentomaxillofac Radiol. 2017;46:20160434.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Attyé A, Troprès I, Rouchy RC, Righini C, Espinoza S, Kastler A, et al. Diffusion MRI: literature review in salivary gland tumors. Oral Dis. 2017;23:572–5.

    Article  PubMed  Google Scholar 

  24. 24.

    Eida S, Sumi M, Sakihama N, Takahashi H, Nakamura T. Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy. AJNR Am J Neuroradiol. 2007;28:116–21.

    PubMed  Google Scholar 

  25. 25.

    Habermann CR, Arndt C, Graessner J, Diestel L, Petersen KU, Reitmeier F, et al. Diffusion-weighted echo-planar MR imaging of primary parotid gland tumors: is a prediction of different histologic subtypes possible? AJNR Am J Neuroradiol. 2009;30:591–6.

    Article  PubMed  Google Scholar 

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Correspondence to Ahmed Abdel Khalek Abdel Razek.

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Ahmed Abdel Khalek Abdel Razek declares no conflict of interest.

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Razek, A.A.K.A. Prediction of malignancy of submandibular gland tumors with apparent diffusion coefficient. Oral Radiol 35, 11–15 (2019).

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  • Diffusion
  • Magnetic resonance imaging
  • Submandibular
  • Tumor