Literature DB >> 23703801

Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla.

Julia Fruehwald-Pallamar1, Christian Czerny, Laura Holzer-Fruehwald, Stefan F Nemec, Christina Mueller-Mang, Michael Weber, Marius E Mayerhoefer.   

Abstract

The purpose of this study was to evaluate whether texture-based analysis of standard MRI sequences and diffusion-weighted imaging can help in the discrimination of parotid gland masses. The MR images of 38 patients with a biopsy- or surgery-proven parotid gland mass were retrospectively analyzed. All patients were examined on the same 3.0 Tesla MR unit, with one standard protocol. The ADC (apparent diffusion coefficient) values of the tumors were measured with three regions of interest (ROIs) covering the entire tumor. Texture-based analysis was performed with the texture analysis software MaZda (version 4.7), with ROI measurements covering the entire tumor in three slices. COC (co-occurrence matrix), RUN (run-length matrix), GRA (gradient), ARM (auto-regressive model), and WAV (wavelet transform) features were calculated for all ROIs. Three subsets of 10 texture features each were used for a linear discriminant analysis (LDA) in combination with k nearest neighbor classification (k-NN). Using histology as a standard of reference, benign tumors, including subtypes, and malignant tumors were compared with regard to ADC and texture-based values, with a one-way analysis of variance with post-hoc t-tests. Significant differences were found in the mean ADC values between Warthin tumors and pleomorphic adenomas, as well as between Warthin tumors and benign lesions. Contrast-enhanced T1-weighted images contained the most relevant textural information for the discrimination between benign and malignant parotid masses, and also for the discrimination between pleomorphic adenomas and Warthin tumors. STIR images contained the least relevant texture features, particularly for the discrimination between pleomorphic adenomas and Warthin tumors. Texture analysis proved to differentiate benign from malignant lesions, as well as pleomorphic adenomas from Warthin tumors, based on standard T(1w) sequences (without and with contrast). Of all benign parotid masses, Warthin tumors had significantly lower ADC values than the other entities.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  3T MRI; diffusion-weighted MRI; parotid gland tumors; parotid tumor; texture-based analysis

Mesh:

Year:  2013        PMID: 23703801     DOI: 10.1002/nbm.2962

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  41 in total

1.  Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors.

Authors:  Gao Ma; Liu-Ning Zhu; Guo-Yi Su; Hao Hu; Wen Qian; Shou-Shan Bu; Xiao-Quan Xu; Fei-Yun Wu
Journal:  Eur Arch Otorhinolaryngol       Date:  2018-07-02       Impact factor: 2.503

2.  Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results.

Authors:  Maria Adele Marino; Katja Pinker; Doris Leithner; Janice Sung; Daly Avendano; Elizabeth A Morris; Maxine Jochelson
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

3.  MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma.

Authors:  M Dang; J T Lysack; T Wu; T W Matthews; S P Chandarana; N T Brockton; P Bose; G Bansal; H Cheng; J R Mitchell; J C Dort
Journal:  AJNR Am J Neuroradiol       Date:  2014-09-25       Impact factor: 3.825

4.  Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT.

Authors:  Valerio Nardone; Paolo Tini; Christophe Nioche; Maria Antonietta Mazzei; Tommaso Carfagno; Giuseppe Battaglia; Pierpaolo Pastina; Roberta Grassi; Lucio Sebaste; Luigi Pirtoli
Journal:  Radiol Med       Date:  2018-01-24       Impact factor: 3.469

Review 5.  Apparent diffusion coefficient measurement of the parotid gland parenchyma.

Authors:  Maja Bruvo; Faisal Mahmood
Journal:  Quant Imaging Med Surg       Date:  2021-08

6.  MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

Authors:  Jian Guo; Zhenyu Liu; Chen Shen; Zheng Li; Fei Yan; Jie Tian; Junfang Xian
Journal:  Eur Radiol       Date:  2018-04-09       Impact factor: 5.315

7.  Perfusion imaging of parotid gland tumours: usefulness of arterial spin labeling for differentiating Warthin's tumours.

Authors:  Hiroki Kato; Masayuki Kanematsu; Haruo Watanabe; Kimihiro Kajita; Keisuke Mizuta; Mitsuhiro Aoki; Tomoyuki Okuaki
Journal:  Eur Radiol       Date:  2015-04-29       Impact factor: 5.315

8.  Magnetic resonance image biomarkers improve differentiation of benign and malignant parotid tumors through diagnostic model analysis.

Authors:  Yuebo Liu; Jiabao Zheng; Jizhi Zhao; Lijiang Yu; Xiaoping Lu; Zhihui Zhu; Chunlan Guo; Tao Zhang
Journal:  Oral Radiol       Date:  2021-01-11       Impact factor: 1.852

9.  Parotid gland lesions: separate and combined diagnostic value of conventional MRI, diffusion-weighted imaging and dynamic contrast-enhanced MRI.

Authors:  Ying Yuan; Weiqing Tang; Xiaofeng Tao
Journal:  Br J Radiol       Date:  2016-02-19       Impact factor: 3.039

10.  Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients.

Authors:  Ahmad Chaddad; Camel Tanougast
Journal:  Med Biol Eng Comput       Date:  2016-03-10       Impact factor: 2.602

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