Gastric cancer differentiation using Fourier transform near-infrared spectroscopy with unsupervised pattern recognition

The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000–7000 cm−1), CH-stretching first overtone (6000–5200 cm−1), and CH-stretching combination (4500–4000 cm−1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.

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► Major spectral differences were observed in three regions.
► Unsupervised pattern recognition techniques (PCA and CA) were used.
► The sensitivity, specificity and accuracy were 100%, 68.2% and 81.1%, respectively.