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脑胶质瘤的纹理分析:文献综述。

Texture Analysis in Cerebral Gliomas: A Review of the Literature.

机构信息

From the Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa.

出版信息

AJNR Am J Neuroradiol. 2019 Jun;40(6):928-934. doi: 10.3174/ajnr.A6075. Epub 2019 May 23.

Abstract

Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging.

摘要

纹理分析是一种不断发展的、非侵入性的放射组学技术,可定量分析宏观组织异质性,这种异质性与人类肉眼无法观察到的微观组织异质性间接相关。近年来,纹理分析在系统肿瘤学中的应用得到了越来越多的探索。本文讨论了纹理分析的基本概念和方法,并综述了胶质瘤成像中各种磁共振成像纹理分析的应用。我们还讨论了磁共振成像纹理分析的局限性和技术挑战,这些问题阻碍了其广泛的临床应用。随着计算处理的不断进步,磁共振成像纹理分析有可能成为常规肿瘤成像中一种有价值的临床工具。

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本文引用的文献

1
Magnetic Resonance Imaging-guided Stereotactic Biopsy: A Review of 83 Cases with Outcomes.
Asian J Neurosurg. 2019 Jan-Mar;14(1):90-95. doi: 10.4103/ajns.AJNS_81_17.
2
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Eur J Radiol. 2019 Mar;112:169-179. doi: 10.1016/j.ejrad.2019.01.025. Epub 2019 Jan 24.
3
Texture Analysis of Imaging: What Radiologists Need to Know.
AJR Am J Roentgenol. 2019 Mar;212(3):520-528. doi: 10.2214/AJR.18.20624. Epub 2019 Jan 15.
5
Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.
J Appl Clin Med Phys. 2018 Nov;19(6):253-264. doi: 10.1002/acm2.12482. Epub 2018 Oct 27.
6
Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain.
Contrast Media Mol Imaging. 2018 Jul 30;2018:1729071. doi: 10.1155/2018/1729071. eCollection 2018.
7
Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis.
Clin Neurol Neurosurg. 2018 Oct;173:84-90. doi: 10.1016/j.clineuro.2018.08.004. Epub 2018 Aug 2.
8
Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time.
IEEE J Biomed Health Inform. 2019 Mar;23(2):795-804. doi: 10.1109/JBHI.2018.2825027. Epub 2018 Apr 9.
10
Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis.
Acta Radiol. 2019 Mar;60(3):356-366. doi: 10.1177/0284185118780889. Epub 2018 Jun 3.

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