Literature DB >> 32703544

MRI-based texture analysis to differentiate the most common parotid tumours.

O Sarioglu1, F C Sarioglu2, A I Akdogan3, U Kucuk4, I B Arslan5, I Cukurova5, Y Pekcevik3.   

Abstract

AIM: To evaluate magnetic resonance imaging (MRI) features and signal characteristics of parotid masses and investigate the added role of texture analysis (TA) in the differentiation of parotid tumours.
MATERIALS AND METHODS: Ninety-five patients (42 women, 53 men; mean age 51.67±14.15) were included in this study. The study group consisted of 40 pleomorphic adenoma, 45 Warthin's tumour, and 10 mucoepidermoid carcinomas. Two reviewers assessed the MRI sequences retrospectively. Fat-suppressed T2-weighted and contrast-enhanced T1-weighted axial images were used for TA. Receiver operating characteristic curve analyses were performed to evaluate the ability to make a diagnosis. Logistic regression analyses were conducted to explore the independent risk factors among the MRI features and to analyse the added value of TA to the qualitative analysis.
RESULTS: Significant differences were found in the tumour border (p<0.001), infiltration of the surrounding tissue (p=0.003), contrast-enhancement grading (p<0.001), perineural spread (p=0.013), and pathological lymph nodes (p<0.001) between the malignant and benign tumours. Kurtosis on contrast-enhanced T1-weighted images, and skewness and kurtosis on T2-weighted images were significantly different between the three groups (p=0.020, <0.001, 0.003; respectively). A kurtosis value on T2-weighted images <2.815 along with an ill-defined border had the highest specificity (98.8%) and positive predictive value (83.3%) in the differentiation of malignant tumours.
CONCLUSION: The addition of TA parameters to the MRI findings may contribute to distinguish benign from malignant parotid tumours.
Copyright © 2020 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2020        PMID: 32703544     DOI: 10.1016/j.crad.2020.06.018

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  7 in total

1.  Apparent Diffusion Coefficient Map-Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors.

Authors:  Baohong Wen; Zanxia Zhang; Jing Zhu; Liang Liu; Yinhua Li; Haoyu Huang; Yong Zhang; Jingliang Cheng
Journal:  Front Oncol       Date:  2022-06-07       Impact factor: 5.738

2.  The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia.

Authors:  Fatma Ceren Sarioglu; Orkun Sarioglu; Handan Guleryuz; Burak Deliloglu; Funda Tuzun; Nuray Duman; Hasan Ozkan
Journal:  Br J Radiol       Date:  2022-01-27       Impact factor: 3.629

3.  MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.

Authors:  Francesca Piludu; Simona Marzi; Marco Ravanelli; Raul Pellini; Renato Covello; Irene Terrenato; Davide Farina; Riccardo Campora; Valentina Ferrazzoli; Antonello Vidiri
Journal:  Front Oncol       Date:  2021-04-27       Impact factor: 6.244

4.  Clot-based radiomics features predict first pass effect in acute ischemic stroke.

Authors:  Orkun Sarioglu; Fatma C Sarioglu; Ahmet E Capar; Demet Fb Sokmez; Berna D Mete; Umit Belet
Journal:  Interv Neuroradiol       Date:  2021-05-18       Impact factor: 1.764

5.  Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.

Authors:  Jinbo Qi; Ankang Gao; Xiaoyue Ma; Yang Song; Guohua Zhao; Jie Bai; Eryuan Gao; Kai Zhao; Baohong Wen; Yong Zhang; Jingliang Cheng
Journal:  Front Oncol       Date:  2022-07-11       Impact factor: 5.738

6.  Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images.

Authors:  Ziyang Hu; Baixin Wang; Xiao Pan; Dantong Cao; Antian Gao; Xudong Yang; Ying Chen; Zitong Lin
Journal:  Front Oncol       Date:  2022-08-01       Impact factor: 5.738

7.  Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.

Authors:  Lorenzo Faggioni; Michela Gabelloni; Fabrizio De Vietro; Jessica Frey; Vincenzo Mendola; Diletta Cavallero; Rita Borgheresi; Lorenzo Tumminello; Jorge Shortrede; Riccardo Morganti; Veronica Seccia; Francesca Coppola; Dania Cioni; Emanuele Neri
Journal:  Eur J Radiol Open       Date:  2022-06-18
  7 in total

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