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: K trans (std) & f(std), K trans (std) & D(std), k ep (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.

A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: A pilot study / F. Patella, G. Franceschelli, M. Petrillo, M. Sansone, R. Fusco, F. Pesapane, G. Pompili, A.M. Ierardi, A.M. Saibene, L. Moneghini, F. Biglioli, G. Carrafiello. - In: FUTURE ONCOLOGY. - ISSN 1479-6694. - 14:28(2018 Dec), pp. 2893-2903. [10.2217/fon-2017-0655]

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

A.M. Saibene;F. Biglioli
Penultimo
;
G. Carrafiello
Ultimo
2018

Abstract

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: K trans (std) & f(std), K trans (std) & D(std), k ep (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.
DCE-MRI; DW-MRI; extended Tofts-Kermode model; intravoxel incoherent motionparotid tumors; pleomorphic adenoma; Warthin tumors; Adult; Aged; Algorithms; Contrast Media; Diagnosis, Differential; Female; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Male; Middle Aged; Parotid Neoplasms; Pilot Projects; Reproducibility of Results; Diffusion Magnetic Resonance Imaging; Image Enhancement; Magnetic Resonance Imaging
Settore MED/36 - Diagnostica per Immagini e Radioterapia
Settore MED/31 - Otorinolaringoiatria
dic-2018
9-feb-2018
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/724607
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