Literature DB >> 28323371

Diffusional kurtosis imaging of parotid glands in Sjögren's syndrome: Initial findings.

Chen Chu1, Huayong Zhang2, Nan Zhou1, Xin Dou1, Ming Li1, Song Liu1, Yun Zhu2, Chun Wang2, Weibo Chen3, Queenie Chan4, Lingyun Sun2, Jian He1, Zhengyang Zhou1.   

Abstract

PURPOSE: To explore the role of diffusion kurtosis imaging (DKI) of parotid glands in diagnosing Sjögren's syndrome (SS).
MATERIALS AND METHODS: A total of 40 patients with SS and 40 healthy volunteers underwent 3.0T magnetic resonance imaging (MRI) including DKI, which generated the apparent diffusion coefficient (ADC), corrected diffusion (D), and diffusional kurtosis (K) values. The MR nodular grade was determined on the basis of MR morphological findings.
RESULTS: The parotid ADC, D, and K values in patients with SS were significantly higher than those of healthy volunteers (P = 0.011, < 0.001, 0.022, respectively). The parotid ADC and D values in patients with SS of MR nodular grade 0 were significantly higher than those of healthy volunteers (all P < 0.001). The parotid D value showed an accuracy of 75.0% and 87.9% in diagnosing patients with SS and MR nodular grade 0, respectively. The parotid ADC and D values correlated negatively, while the K values correlated positively with the MR nodular grade significantly in patients with SS (r = -0.741, -0.605, 0.424, all P < 0.001). All parotid DKI parameters differed significantly among patients with SS at different MR nodular grades (all P < 0.001).
CONCLUSION: Parotid DKI parameters hold great potential in diagnosing SS, especially in early-stage SS without MR morphological changes. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1409-1417.
© 2017 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  Sjögren's syndrome; diagnosis; diffusion kurtosis imaging; magnetic resonance; parotid gland

Mesh:

Year:  2017        PMID: 28323371     DOI: 10.1002/jmri.25697

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  5 in total

1.  Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images.

Authors:  Yoshitaka Kise; Haruka Ikeda; Takeshi Fujii; Motoki Fukuda; Yoshiko Ariji; Hiroshi Fujita; Akitoshi Katsumata; Eiichiro Ariji
Journal:  Dentomaxillofac Radiol       Date:  2019-05-22       Impact factor: 2.419

2.  Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images.

Authors:  Yoshitaka Kise; Mayumi Shimizu; Haruka Ikeda; Takeshi Fujii; Chiaki Kuwada; Masako Nishiyama; Takuma Funakoshi; Yoshiko Ariji; Hiroshi Fujita; Akitoshi Katsumata; Kazunori Yoshiura; Eiichiro Ariji
Journal:  Dentomaxillofac Radiol       Date:  2019-12-11       Impact factor: 2.419

3.  The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor.

Authors:  Peiqian Chen; Bing Dong; Chunye Zhang; Xiaofeng Tao; Pingzhong Wang; Ling Zhu
Journal:  Dentomaxillofac Radiol       Date:  2020-03-09       Impact factor: 2.419

4.  Whole-Volume ADC Histogram Analysis in Parotid Glands to Identify Patients with Sjögren's Syndrome.

Authors:  Chen Chu; Qianqian Feng; Huayong Zhang; Yun Zhu; Weibo Chen; Jian He; Lingyun Sun; Zhengyang Zhou
Journal:  Sci Rep       Date:  2019-07-03       Impact factor: 4.379

5.  Whole-volume ADC Histogram and Texture Analyses of Parotid Glands as an Image Biomarker in Evaluating Disease Activity of Primary Sjögren's Syndrome.

Authors:  Chen Chu; Fengxian Wang; Huayong Zhang; Yun Zhu; Chun Wang; Weibo Chen; Jian He; Lingyun Sun; Zhengyang Zhou
Journal:  Sci Rep       Date:  2018-10-18       Impact factor: 4.379

  5 in total

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