Discrimination of normal and malignant mucosal tissues of the colon by Raman spectroscopy.

OBJECTIVE

The objective of this study was to evaluate the applicability of the discrimination parameters Mahalanobis distance, spectral residuals, and limit tests, developed by this group to differentiate normal from malignant colon tissues.

BACKGROUND DATA

Colon cancers are diagnosed using fiberoptic endoscopic localization and a subsequent histopathological examination of biopsied tissue, which is highly dependent on the skill and experience of the investigator. There exists a risk of missing significant lesions, especially with carcinoma in situ lesions. Raman spectroscopy, which is sensitive to biochemical variations in the samples and amenable to multivariate statistical tools, can lead to rapid and objective detection of colon cancer.

METHODS

A total of 102 spectra from 11 normal and 11 malignant ex vivo colon tissues were recorded by conventional near infrared (NIR) Raman spectroscopy (excitation wavelength of 785 nm). Spectral data were analyzed by principal components analysis (PCA) and other discriminating parameters, namely Mahalanobis distance, spectral residuals, and a multiparametric limit test approach.

RESULTS

Mean malignant spectra exhibit relatively stronger bands, suggesting the presence of additional biomolecules such as protein (stronger amide III and I), lipids (1,100, 1,300 cm(1)), and DNA (1,340, 1,470 cm(1)) versus those seen in normal tissue. Mean normal spectra indicate the presence of disordered structures (hump at 1,247 cm(1)). Scores of factor 1 gave good discrimination, and this is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. A limit test approach provided unambiguous objective discrimination.

CONCLUSION

This study further supports the efficacy of Raman spectroscopy, in combination with a limit test, for discrimination of normal and malignant colon tissues. The multiparametric limit test approach is user-friendly, and a clinician or minimally trained individual could directly compare the unknown spectra against the available standard sets to make the decision instantly, objectively, and unambiguously.