The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

• Published in: Physics in medicine & biology Vol. 54; no. 16; p. 4945
• Main Authors: Theodorakou, Chrysoula, Farquharson, Michael J
• Format: Journal Article
• Language: English
• Published: England 21-08-2009
• Subjects: Biopsy; Colorectal Neoplasms - pathology; Humans; Liver - cytology; Liver - pathology; Liver Neoplasms - classification; Liver Neoplasms - pathology; Liver Neoplasms - secondary; Multivariate Analysis; X-Ray Diffraction
• ISSN: 0031-9155
• DOI: 10.1088/0031-9155/54/16/007

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.