Literature DB >> 29908348

Diagnostic accuracy of magnetic resonance imaging techniques for parotid tumors, a systematic review and meta-analysis.

Ying-Ying Liang1, Fan Xu2, Yuan Guo1, Jin Wang3.   

Abstract

OBJECTIVE: To assess the added benefit of combining different MRI techniques for preoperative diagnosis of parotid tumors when compared to conventional MRI and advanced MRI techniques alone with meta-analysis.
METHODS: A comprehensive PubMed electronic database search was performed for original diagnostic studies up to July 2017. The methodologic quality of each study was evaluated by two independent reviewers who used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Statistical analysis included pooling of sensitivity and specificity with 95% confidence intervals (CI). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs.
RESULTS: Pooled sensitivity and specificity of conventional MRI, diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and the above combination were 76% (95%CI)/91% (95%CI)/80% (95%CI)/86% (95%CI) and 83% (95%CI)/56% (95%CI)/90% (95%CI)/90% (95%CI).
CONCLUSION: Conventional MRI combined with DWI and DCE showed higher diagnostic accuracy than conventional or advanced MRI alone, supporting their use in parotid tumors diagnosis.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diffusion weighted imaging; Dynamic contrast enhanced; Magnetic resonance imaging; Parotid tumor

Mesh:

Substances:

Year:  2018        PMID: 29908348     DOI: 10.1016/j.clinimag.2018.05.026

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  8 in total

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Authors:  Sheng Wang; Zijian Zhou; Zhantong Wang; Yijing Liu; Orit Jacobson; Zheyu Shen; Xiao Fu; Zhi-Yi Chen; Xiaoyuan Chen
Journal:  Small       Date:  2019-03-26       Impact factor: 13.281

2.  Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors.

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3.  Point-of-care ultrasound scan as the primary modality for evaluating parotid tumors.

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Journal:  Laryngoscope Investig Otolaryngol       Date:  2022-08-11

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

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5.  Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis.

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Review 6.  Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors - An updated literature review.

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Journal:  Bosn J Basic Med Sci       Date:  2021-02-01       Impact factor: 3.363

7.  Feasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland Tumors.

Authors:  Karolina Markiet; Anna Glinska; Tomasz Nowicki; Edyta Szurowska; Boguslaw Mikaszewski
Journal:  Biology (Basel)       Date:  2022-03-04

8.  Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images.

Authors:  Xianwu Xia; Bin Feng; Jiazhou Wang; Qianjin Hua; Yide Yang; Liang Sheng; Yonghua Mou; Weigang Hu
Journal:  Front Oncol       Date:  2021-06-23       Impact factor: 6.244

  8 in total

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