We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×
Skip main navigation
Aging Health
Bioelectronics in Medicine
Biomarkers in Medicine
Breast Cancer Management
CNS Oncology
Colorectal Cancer
Concussion
Epigenomics
Future Cardiology
Future Medicine AI
Future Microbiology
Future Neurology
Future Oncology
Future Rare Diseases
Future Virology
Hepatic Oncology
HIV Therapy
Immunotherapy
International Journal of Endocrine Oncology
International Journal of Hematologic Oncology
Journal of 3D Printing in Medicine
Lung Cancer Management
Melanoma Management
Nanomedicine
Neurodegenerative Disease Management
Pain Management
Pediatric Health
Personalized Medicine
Pharmacogenomics
Regenerative Medicine

A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: a pilot study

    Francesca Patella

    *Author for correspondence:

    E-mail Address: battellina@gmail.com

    Postgraduation School of Radiodiagnostic of Milan, Università degli Studi di Milano, Milan, Italy

    ,
    Giuseppe Franceschelli

    Diagnostic & Interventional Radiology Service, San Paolo Hospital, Milan, Italy

    ,
    Mario Petrillo

    Diagnostic & Interventional Radiology Service, San Paolo Hospital, Milan, Italy

    ,
    Mario Sansone

    Department of Electrical Engineering & Information Technologies, University “Federico II” of Naples, Via Claudio, Naples, Italy

    ,
    Roberta Fusco

    Radiology Unit, “Dipartimento di supporto ai percorsi oncologici Area Diagnostica, Istituto Nazionale Tumori – IRCCS – Fondazione G Pascale”, Via Mariano Semmola, Naples, Italy

    ,
    Filippo Pesapane

    Postgraduation School of Radiodiagnostic of Milan, Università degli Studi di Milano, Milan, Italy

    ,
    Giovanni Pompili

    Diagnostic & Interventional Radiology Service, San Paolo Hospital, Milan, Italy

    ,
    Anna Maria Ierardi

    Diagnostic & Interventional Radiology Service, San Paolo Hospital, Milan, Italy

    ,
    Alberto Maria Saibene

    Otolaryngology Unit, ASST Santi Paolo e Carlo, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy

    ,
    Laura Moneghini

    Department of Health Sciences, Division of Pathology, University of Milan, AO Santi Paolo e Carlo, 20142 Milan, Italy

    ,
    Federico Biglioli

    Maxillofacial Surgery Unit, ASST Santi Paolo e Carlo, Università degli Studi di Milano, Milan, Italy

    &
    Gianpaolo Carrafiello

    Diagnostic & Interventional Radiology Service, San Paolo Hospital, Milan, Italy

    Published Online:https://doi.org/10.2217/fon-2017-0655

    Aim: To evaluate dynamic contrast-enhanced (DCE)-MRI and diffusion weighted (DW)-MRI diagnostic value to differentiate Warthin tumors (WT) by pleomorphic adenomas (PA). Materials & methods: Seven WT and seven PA were examined. DCE- and  DW-MRI parameters were extracted from volumes of interest; volume of interest-based averages and standard deviations were calculated. Statistical analysis included: linear discriminant analysis, receiver operating characteristic curves, sensitivity and specificity. Results: No single feature was able to differentiate WT by PA (p > 0.05); linear discriminant analysis analysis showed that a combination of all features or combinations of feature pairs (namely: Ktrans(std) & f(std), Ktrans(std) & D(std), kep(std) & D(std), MRE(av) & TTP(av)) might achieve sensitivity (SENS), specificity (SPEC) = 100%, with a slight reduction after cross-validation analysis (SENS = 0.875; SPEC = 1). Conclusion: Although preliminary and not conclusive, our results suggest that differentiation between WT and PA is possible through a multiparametric approach based on combination of DCE- and DW-MRI parameters.

    References

    • 1 Barnes L, Eveson JW, Reichart P, Sidransky D. World Health Organization classification of tumors – pathology and genetics of head and neck tumors. IARC (2005). www.iarc.fr/en/publications/pdfs-online/pat-gen/bb9/BB9.pdf.
    • 2 Gao M, Hao Y, Huang MX et al. Salivary gland tumors in a northern Chinese population: a 50-year retrospective study of 7190 cases. Int. J. Oral Maxillofac. Surg. 46(3), 343–349 (2017).
    • 3 Aparecida De Oliveira F, Barroso Duarte EC, Taveira TC et al. Salivary gland tumor: a review of 599 cases in a Brazilian population. www.ncbi.nlm.nih.gov/pmc/articles/PMC2811571/pdf/12105_2009_Article_139.pdf.
    • 4 Lukšić I, Virag M, Manojlović S et al. Salivary gland tumors: 25 years of experience from a single institution in Croatia. J. Craniomaxillofac. Surg. 40(3), e75–e81 (2012).
    • 5 Som PM, Curtin HD. Head and Neck Imaging (4th Edition). Mosby, MI, USA (2000).https://books.google.co.uk/books?id=QHA7Fah1HEIC.
    • 6 Di Palma S. Carcinoma ex pleomorphic adenoma, with particular emphasis on early lesions. Head Neck Pathol. 7(Suppl. 1), S68–S76 (2013).
    • 7 Witt RL, Eisele DW, Morton RP, Nicolai P, Vander Poorten V, Zbären P. Etiology and management of recurrent parotid pleomorphic adenoma. Laryngoscope 125(4), 888–893 (2015).
    • 8 Bradley PT, Paleri V, Homer JJ. Consensus statement by otolaryngologists on the diagnosis and management of benign parotid gland disease. Clin. Otolaryngol. 37(4), 300–304 (2012).
    • 9 Bozzetti A, Biglioli F, Salvato G, Brusati R. Technical refinements in surgical treatment of benign parotid tumors. J. Cranio-Maxillofacial Surg. 27(5), 289–293 (1999).
    • 10 Riad MA, Abdel-Rahman H, Ezzat WF, Adly A, Dessouky O, Shehata M. Variables related to recurrence of pleomorphic adenomas: outcome of parotid surgery in 182 cases. Laryngoscope 121(7), 1467–1472 (2011).
    • 11 Wang H, Fundakowski C, Khurana JS, Jhala N. Fine-needle aspiration biopsy of salivary gland lesions. Arch. Pathol. Lab. Med. 139(12), 1491–1497 (2015).
    • 12 Abouyared M, Fundakowski C, Casta J et al. The role of indeterminate fine-needle biopsy in the diagnosis of parotid malignancy. Laryngoscope 124(3), 678–681 (2014).
    • 13 Yabuuchi H, Matsuo Y, Kamitani T et al. Parotid gland tumors: can addition of diffusion-weighted MRI to dynamic contrast-enhanced MRI improve diagnostic accuracy in characterization? Radiology 249(3), 909–916 (2008).
    • 14 Yabuuchi H, Fukuya T, Tajima T, Hachitanda Y, Tomita K, Koga M. Salivary gland tumors: diagnostic value of gadolinium-enhanced dynamic MRI with histopathologic correlation. Radiology 226, 345–354 (2003).
    • 15 Espinoza S, Halimi P. Interpretation pearls for MRI of parotid gland tumor. Eur. Ann. Otorhinolaryngol. Head Neck Dis. 130(1), 30–35 (2013).
    • 16 Mikaszewski B, Markiet K, Smugała A, Stodulski D, Szurowska E, Stankiewicz C. Diffusion- and perfusion-weighted magnetic resonance imaging – an alternative to fine needle biopsy or only an adjunct test in preoperative differential diagnostics of malignant and benign parotid tumors? J. Oral Maxillofac. Surg. 75(10), 2248–2253 (2017).
    • 17 Tofts P. T1-weighted DCE imaging concepts: modelling, acquisition and analysis. www.paul-tofts-phd.org.uk/DCE-MRI_siemens.pdf.
    • 18 Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion MRI can tell us about tissue structure. Radiology 268(2), 318–322 (2013).
    • 19 Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MRI. Radiology 168(2), 497–505 (1988).
    • 20 Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MRI: past, present and future. Radiology 278(1), 13–32 (2016).
    • 21 Kato H, Fujimoto K, Matsuo M, Mizuta K, Aoki M. Usefulness of diffusion-weighted MRI for differentiating between Warthin's tumor and oncocytoma of the parotid gland. Jpn J. Radiol. 35(2), 78–85 (2017).
    • 22 Abdel Razek AAK, Samir S, Ashmalla GA. Characterization of parotid tumors with dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging and diffusion-weighted MRI. J. Comput. Assist. Tomogr. 41(1), 131–136 (2017).
    • 23 Sumi M, Nakamura T. Head and neck tumors: combined MRI assessment based on IVIM and TIC analyses for the differentiation of tumors of different histological types. Eur. Radiol. 24(1), 223–231 (2014).
    • 24 Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J. Magn. Reson. Imaging 7(1), 91–101 (1997).
    • 25 Brix G, Semmler W, Port R, Schad LR, Layer G, Lorenz WJ. Pharmacokinetic parameters in CNS Gd-DTPA enhanced MRI. J. Comput. Assist. Tomogr. 15(4), 621–628 (1991).
    • 26 Weinmann HJ, Laniado M, Mützel W. Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers. Physiol. Chem. Phys. Med. NMR 16(2), 167–172 (1984).
    • 27 Schabel MC, Morrell GR, Oh KY, Walczak CA, Barlow RB, Neumayer LA. Pharmacokinetic mapping for lesion classification in dynamic breast MRI. J. Magn. Reson. Imaging. 31(6), 1371–1378 (2010).
    • 28 Fusco R, Petrillo A, Petrillo M, Sansone M. Use of tracer kinetic models for selection of semiquantitative features for DCE-MRI data classification. Appl. Magn. Reson. 44(11), 1311–1324 (2013).
    • 29 Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MRI of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161(2), 401–407 (1986).
    • 30 Vignaud A, Cavet M. Liver cirrhosis: intravoxel incoherent motion MRI – pilot purpose: methods: results: conclusion. Radiology 249(3), 891–899 (2008).
    • 31 Fusco R, Sansone M, Petrillo A. The use of the Levenberg–Marquardt and variable projection curve-fitting algorithm in intravoxel incoherent motion method for DW-MRI data analysis. Appl. Magn. Reson. 46(5), 551–558 (2015).
    • 32 Fusco R, Sansone M, Petrillo A. A comparison of fitting algorithms for diffusion-weighted MRI data analysis using an intravoxel incoherent motion model. MAGMA 30(2), 113–120 (2017).
    • 33 Callot V, Bennett E, Decking UKM, Balaban RS, Wen H. In vivo study of microcirculation in canine myocardium using the IVIM method. Magn. Reson. Med. 50(3), 531–540 (2003).
    • 34 Yao L, Sinha U. Imaging the microcirculatory proton fraction of muscle with diffusion-weighted echo-planar imaging. Acad. Radiol. 7(1), 27–32 (2000).
    • 35 Wirestam R, Borg M, Brockstedt S, Lindgren A, Holtås S, Ståhlberg F. Perfusion-related parameters in intravoxel incoherent motion MRI compared with CBV and CBF measured by dynamic susceptibility-contrast MR technique. Acta Radiol. 42(2), 123–128 (2001).
    • 36 Moteki T, Horikoshi H. Evaluation of hepatic lesions and hepatic parenchyma using diffusion-weighted echo-planar MR with three values of gradient b-factor. J. Magn. Reson. Imaging 24(3), 637–645 (2006).
    • 37 Granata V, Fusco R, Catalano O et al. Intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (DWI) for hepatocellular carcinoma: correlation with histologic grade. Oncotarget 7(48), 79357–79364 (2016).
    • 38 Krzanowski WJ. Principles of Multivariate Analysis. A User's Perspective. Oxford, NY, USA (2000).
    • 39 Fusco R, Sansone M, Filice S et al. Pattern recognition approaches for breast cancer DCE-MRI classification: a systematic review. J. Med. Biol. Eng. 36(4), 449–459 (2016).
    • 40 Fusco R, Sansone M, Filice S et al. Integration of DCE-MRI and DW-MRI quantitative parameters for breast lesion classification. Biomed. Res. Int. 2015, 237863 (2015).
    • 41 Bishop CM. Pattern recognition and machine learning. www.library.wisc.edu/selectedtocs/bg0137.pdf.
    • 42 Lam PD, Kuribayashi A, Imaizumi A et al. Differentiating benign and malignant salivary gland tumors: diagnostic criteria and the accuracy of dynamic contrast-enhanced MRI with high temporal resolution. Br. J. Radiol. 88(1049), 8–12 (2015).
    • 43 Assili S, Fathi Kazerooni A, Aghaghazvini L, Saligheh Rad HR, Pirayesh Islamian J. Dynamic contrast magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DWI) for differentiation between benign and malignant salivary gland tumors. J. Biomed. Phys. Eng. 5(4), 157–168 (2015).
    • 44 Pesapane F, Patella F, Maria E et al. Intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) in the periferic prostate cancer detection and stratification. Med. Oncol. 34(3), 35 (2017).
    • 45 Federau C, Cerny M, Roux M et al. IVIM perfusion fraction is prognostic for survival in brain glioma. Clin. Neuroradiol. 27(4), 485–492 (2016).
    • 46 Sumi M, Van Cauteren M, Sumi T, Obara M, Ichikawa Y, Nakamura T. Salivary gland tumors: use of intravoxel incoherent motion MRI for assessment of diffusion and perfusion for the differentiation of benign from malignant tumors. Radiology 263(3), 770–777 (2012).
    • 47 Padhani AR, Liu G, Koh DM et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11(2), 102–125 (2009).
    • 48 Bernstein JM, Homer JJ, West CM. Dynamic contrast-enhanced magnetic resonance imaging biomarkers in head and neck cancer: potential to guide treatment? A systematic review. Oral Oncol. 50(10), 963–970 (2014).
    • 49 Noij DP, De Jong MC, Mulders LGM et al. Contrast-enhanced perfusion magnetic resonance imaging for head and neck squamous cell carcinoma: a systematic review. Oral Oncol. 51(2), 124–138 (2015).
    • 50 Attyé A, Troprès I, Rouchy R-C et al. Diffusion MRI: literature review in salivary gland tumors. Oral Dis. 37(12), 1412–1416 (2016).
    • 51 Parwani AV, Ali SZ. Diagnostic accuracy and pitfalls in fine-needle aspiration interpretation of Warthin tumor. Cancer 99(3), 166–171 (2003).
    • 52 Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 26(8), 1004–1027 (2013).
    • 53 Luypaert R, Sourbron S, De Mey J. Validity of perfusion parameters obtained using the modified Tofts model: a simulation study. Magn. Reson. Med. 65(5), 1491–1497 (2011).
    • 54 Heisen M, Fan X, Buurman J, Van Riel NAW, Karczmar GS, ter Haar Romeny BM. The influence of temporal resolution in determining pharmacokinetic parameters from DCE-MRI data. Magn. Reson. Med. 63(3), 811–816 (2010).