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Towards a practical Fourier transform infrared chemical imaging protocol for cancer histopathology

机译:迈向癌症组织病理学的实用傅里叶变换红外化学成像方案

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摘要

Fourier transform infrared (FTIR) chemical imaging is a strongly emerging technology that is being increasingly applied to examine tissues in a high-throughput manner. The resulting data quality and quantity have permitted several groups to provide evidence for applicability to cancer pathology. It is critical to understand, however, that an integrated approach with optimal data acquisition, classification, and validation is necessary to realize practical protocols that can be translated to the clinic. Here, we first review the development of technology relevant to clinical translation of FTIR imaging for cancer pathology. The role of each component in this approach is discussed separately by quantitative analysis of the effects of changing parameters on the classification results. We focus on the histology of prostate tissue to illustrate factors in developing a practical protocol for automated histopathology. Next, we demonstrate how these protocols can be used to analyze the effect of experimental parameters on prediction accuracy by analyzing the effects of varying spatial resolution, spectral resolution, and signal to noise ratio. Classification accuracy is shown to depend on the signal to noise ratio of recorded data, while depending only weakly on spectral resolution.
机译:傅里叶变换红外(FTIR)化学成像技术是一项新兴技术,正越来越多地以高通量方式应用于检查组织。由此产生的数据质量和数量已使几个小组提供了适用于癌症病理学的证据。但是,至关重要的是要理解,必须采用具有最佳数据采集,分类和验证功能的集成方法,才能实现可以转化为临床的实用协议。在这里,我们首先回顾与FTIR成像用于癌症病理学的临床翻译相关的技术发展。通过定量分析变化的参数对分类结果的影响,分别讨论了每种方法在该方法中的作用。我们专注于前列腺组织的组织学,以阐明开发自动化组织病理学实用协议的因素。接下来,我们通过分析变化的空间分辨率,频谱分辨率和信噪比的影响,演示如何使用这些协议来分析实验参数对预测精度的影响。示出分类精度取决于记录数据的信噪比,而仅弱地取决于光谱分辨率。

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