Literature DB >> 25115306

Effectiveness of 3 T PROPELLER DUO diffusion-weighted MRI in differentiating sinonasal lymphomas and carcinomas.

X Wang1, Z Zhang1, Q Chen1, J Li1, J Xian2.   

Abstract

AIM: To evaluate the value of 3 T Periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) DUO diffusion-weighted MRI (DW-MRI) in differentiating sinonasal lymphomas from carcinomas.
MATERIALS AND METHODS: PROPELLER DUO DW-MRI was performed in 23 patients with sinonasal lymphomas and 28 patients with carcinomas histologically confirmed at 3 T MRI. Apparent diffusion coefficients (ADCs) were obtained with two different b-values (b = 0 and 700 s/mm(2), b = 0 and 1000 s/mm(2)) and two different regions of interest (ROIs) sampling strategies [whole section (WS), partial section (PS)], respectively. Difference in ADCs between sinonasal lymphomas and carcinomas was evaluated using the independent samples t-test or Mann-Whitney U-test. The utility of ADC thresholds for discriminating between them was evaluated by receiver operating characteristic analysis.
RESULTS: ADCs of sinonasal lymphomas (WS ADCb0,700, 0.838 × 10(-3) mm(2)/s) were significantly (p < 0.001) lower than those of carcinomas (WS ADCb0,700, 1.396 × 10(-3) mm(2)/s). Using a WS ADC b0,700 value of 1.040 × 10(-3) mm(2)/s as the threshold value effectively differentiated sinonasal lymphomas from carcinomas with 100% sensitivity, 82.1% specificity, and 82.1% positive and 100% negative predictive values and 90.2% accuracy (b = 0, 700 s/mm(2)). There was no significant difference in diagnostic ability between different b-values settings (p > 0.05) and different sampling strategies of ROIs (p > 0.05), respectively. Additionally, there was significant differences in the ADCs between diffuse large B-cell lymphomas and natural killer (NK)/T-cell lymphomas (p < 0.05).
CONCLUSION: PROPELLER DUO DW-MRI can effectively differentiate sinonasal lymphomas from carcinomas.
Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2014        PMID: 25115306     DOI: 10.1016/j.crad.2014.07.003

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


  4 in total

1.  Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values.

Authors:  Chen Chen; Yuhui Qin; Haotian Chen; Junying Cheng; Bo He; Yixuan Wan; Dongyong Zhu; Fabao Gao; Xiaoyue Zhou
Journal:  Eur Radiol       Date:  2022-01-14       Impact factor: 7.034

2.  Improved performance in differentiating benign from malignant sinonasal tumors using diffusion-weighted combined with dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Xin-Yan Wang; Fei Yan; Hui Hao; Jian-Xing Wu; Qing-Hua Chen; Jun-Fang Xian
Journal:  Chin Med J (Engl)       Date:  2015-03-05       Impact factor: 2.628

3.  Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors.

Authors:  Chen Chen; Yuhui Qin; Junying Cheng; Fabao Gao; Xiaoyue Zhou
Journal:  Front Oncol       Date:  2021-12-13       Impact factor: 6.244

Review 4.  Imaging Characteristics of Malignant Sinonasal Tumors.

Authors:  Masaya Kawaguchi; Hiroki Kato; Hiroyuki Tomita; Keisuke Mizuta; Mitsuhiro Aoki; Akira Hara; Masayuki Matsuo
Journal:  J Clin Med       Date:  2017-12-06       Impact factor: 4.241

  4 in total

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