The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis
The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis
复制标题
使用角色散 X 射线衍射和多变量分析对人体活检样本中继发性结直肠肝癌进行分类
DOI：
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发表时间：
2009
影响因子：
3.5
通讯作者：
M. Farquharson
中科院分区：
文献类型：
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作者：
C. Theodorakou;M. Farquharson
这项研究背后的动机是评估使用多变量分析技术处理的角色散X射线衍射（ADXRD）数据是否可用于对人类肝活检样本中的继发性结直肠肝癌组织和正常周围肝组织进行分类。用同步辐射光源测量了60例正常肝组织和结直肠癌肝转移灶的ADXRD谱。使用非线性峰拟合软件分析了56个样品的数据。将四个峰与所有ADXRD曲线拟合，并计算四个峰中三个峰的振幅、面积、振幅和面积比，并用于统计和多变量分析。统计学分析表明，正常组织与病变组织的峰拟合参数及比值均存在显著性差异。软独立建模类类比（SIMCA）的技术被用来分类正常肝组织和结直肠肝转移，导致67%的正常组织样本和60%的继发性结直肠肝组织样本被正确分类。这项研究表明，正常和继发性结直肠肝癌的ADXRD数据有统计学差异，使用多变量分析的X射线衍射数据分析有可能被用作组织分类的方法。
The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.