A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors

Abstract

SGTs vary in histological behavior. Mucins, a major component in salivary glands, consist of a glycosylated and sialylated protein core. Rapid evolutions in glycobiology have demonstrated the important role of glycoproteins in cancer development. NIR spectroscopy is a method for the biochemical analysis of substrates. NIR spectra can be analyzed using specific chemometrics. Our aim was to explore the diagnostic possibilities of NIR spectroscopy in SGTs. 238 Hematoxylin and Eosine stained (H&E) SGT tissue sections were examined using NIR spectroscopy. 45 deparaffinized tissue sections were treated with neuraminidase to identify wavelengths in the NIR spectrum related to sialylation. NIR spectra were analyzed with chemometrics. NIR spectra could distinguish malignant SGTs from controls and benign SGTs. Prediction models based on the entire spectral range resulted in a 73.1% accurate classification of malignant SGTs and controls, while, based on neuraminidase experimental spectral peak differences (1436 nm; 1713 nm; 1783 nm; 1924 nm; 2032 nm; 2064 nm; 2178 nm; 2216 nm), an improved overall correct classification rate of 91.9% was obtained between healthy subjects and malignant tumors. H&E tissue section-based NIR spectroscopy can identify malignant SGTs from controls, promising an alternative method in the diagnosis of SGTs.

Identification of gallbladder cancer by direct near-infrared measurement of raw bile combined with two-trace two-dimensional correlation analysis

“…This previously reported NIR bile analysis scheme has potential for clinical on-site analysis due to the simple and rapid analysis that requires no pretreatment of raw bile. Other than the analysis of bile, NIR spectroscopy has been extensively adopted for biomedical diagnosis 2,3 using relevant biological specimens including blood, 4,5 serum, 6–8 saliva, 9 and tissue, 10,11 and diverse chemometric and deep learning methods such as partial least squares (PLS), 5–8,12 linear discriminant analysis (LDA), 3,11,14 support vector machine (SVM), 13 and artificial neural network (ANN) 13 are combined for multivariate analysis of acquired NIR spectra.…”