Disease recognition by infrared and Raman spectroscopy

Abstract

Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis.

Infrared spectroscopy indicates altered bone turnover and remodeling activity in renal osteodystrophy

“…(16,22) In future studies, approaches to minimize the effect of varying sample thickness, as well as to improve the specificity of FTIRI in the diagnostics of bone diseases, would be the investigation of qualitative spectral differences, for example, implementation of multivariate clustering techniques. (37) Clustering analysis may provide the possibility to determine the qualitative biochemical differences, in addition to quantitative composition analysis, between the diseased and normal bone. Additionally, spectral preprocessing methods that account for scattering in IR energy may be beneficial.…”

Correlation of p16INK4A expression and HPV copy number with cellular FTIR spectroscopic signatures of cervical cancer cells

“…It should be noted that, in figure 2, the HPV copy number is represented by the average of the range quoted in literature [57][58] and so error margins in the horizontal axis are potentially very large. However, the sublinear nature of the plot indicates that p16 INK4A expression levels are 15 particularly sensitive for low HPV copy number.…”

“…The use of p16 INK4A immunocytochemical analysis as a complement to conventional screening programmes could potentially aid in the reduction of false positive and false negative results 12 . 5 In the first part of this study, expression of p16 INK4A [13][14][15][16][17][18][19] . The development of applications of vibrational spectroscopy to medical diagnostics has recently been 30 reviewed by Diem et al 20 .…”

Spectroscopic and chemometric approaches to radiobiological analyses

“…The higher signal levels achievable with FTIRM result in increased speed of measurement and consequently higher sample throughput over CRM [21]. The strengths of both are now well established in hyperspectral imaging for non-invasive and label-free histopathology [22][23][24][25][26][27][28][29][30], while the capabilities of CRM to detect tissue abnormalities in-vivo without the complication of contamination of spectral measurements by water and atmospheric features has resulted in a move to the development of clinical devices [31].…”