PURPOSE - Create a Raman spectroscopic database with potential to diagnose cancer and investigate two different diagnostic methodologies. Raman spectroscopy measures the energy of photons scattered inelastically by molecules. These molecular signatures form the basis of identifying complex biomolecules and can be used to differentiate normal from neoplastic tissue.
METHODS - 1,352 spectra from 55 specimens were collected from fresh or frozen normal brain, kidney and adrenal gland and their malignancies. Spectra were obtained utilizing a Renishaw Raman microscope (RM1000) at 785 nm excitation wavelength with an exposure time of 10 to 20 s/spectrum over three accumulations. Spectra were preprocessed and discriminant function analysis was used to classify spectra based on pathological gold standard.
RESULTS - The results of leave 25 % out training/testing validation were as follows: 94.3 % accuracy for training and 91.5 % for testing adrenal, 95.1 % accuracy for training and 88.9 % for testing group of brain, and 100 % accuracy for kidney training/testing groups when tissue origin was assumed. A generalized database not assuming tissue origin provided 88 % training and 85.5 % testing accuracy.
CONCLUSION - A database can be made from Raman spectra to classify and grade normal from cancerous tissue. This database has the potential for real time diagnosis of fresh tissue and can potentially be applied to the operating room in vivo.