Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Insights Imaging. 2012 Dec;3(6):573-89. doi: 10.1007/s13244-012-0196-6. Epub 2012 Oct 24.
Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images
Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.
Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.
This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. TEACHING POINTS : • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.
肿瘤空间异质性是一个重要的预后因素，这可能体现在医学图像上。
图像纹理分析是一种量化异质性的方法，可能无法用肉眼察觉。可以应用不同的方法，包括基于统计、模型和变换的方法。
早期证据表明，纹理分析有可能增强诊断和特征描述，以及改善肿瘤分期和治疗反应评估在肿瘤学实践中。
这篇综述概述了迄今为止不同成像方式（CT、MRI 和 PET）的纹理分析应用，并描述了限制其广泛临床应用的技术挑战。通过进一步努力完善其应用，图像纹理分析有可能成为肿瘤影像学中一种有价值的临床工具。
肿瘤空间异质性是一个重要的预后因素。
图像纹理分析是一种量化异质性的方法。
可以应用不同的方法，包括基于统计、模型和变换的方法。
纹理分析有可能改善诊断、肿瘤分期和治疗反应评估。
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