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The use of combined trace element XRF and EDXRD data as a histopathology tool using a multivariate analysis approach in characterizing breast tissue

机译:结合使用微量元素XRF和EDXRD数据作为组织病理学工具,并使用多变量分析方法来表征乳腺组织

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The concentrations of K, Fe, Cu and Zn were measured in 77 breast tissue samples (38 classified as normal and 39 classified as diseased) using x-ray fluorescence (XRF) techniques. The coherent scattering profiles were also measured using energy-dispersive x-ray diffraction (EDXRD), from which the proportions of adipose and fibrous tissue in the samples were estimated. The data from 30 normal samples and 30 diseased samples were used as a training set to construct two calibration models, one using a partial least-squares (PLS) regression and one using a principal component analysis (PCA) for a soft independent modelling of class analogy (SIMCA) technique. The data from the remaining samples, eight normal and nine diseased, were presented to each model and predictions were made of the tissue characteristics. Three data groups were tested, XRF, EDXRD and a combination of both. The XRF data alone proved to be most unreliable indicator of disease state with both types of analysis. The EDXRD data were an improvement, but with both methods of modelling the ability to predict the tissue type most accurately was by using a combination of the data. Copyright (C) 2004 John Wiley Sons, Ltd.
机译:使用X射线荧光(XRF)技术测量了77个乳腺组织样品(38个分类为正常,39个分类为患病)中的K,Fe,Cu和Zn浓度。还使用能量色散X射线衍射(EDXRD)测量了相干散射曲线,由此估算了样品中脂肪和纤维组织的比例。来自30个正常样本和30个患病样本的数据用作训练集,以构建两个校准模型,一个使用偏最小二乘(PLS)回归,一个使用主成分分析(PCA)进行类的软独立建模类比(SIMCA)技术。其余样本的数据(八个正常和九个患病)被提供给每个模型,并对组织特征进行了预测。测试了三个数据组,即XRF,EDXRD和两者的组合。在两种分析中,仅XRF数据就被证明是最不可靠的疾病状态指标。 EDXRD数据是一种改进,但是使用两种数据建模方法能够最准确地预测组织类型的方法是结合使用数据。版权所有(C)2004 John Wiley Sons,Ltd.

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