Characterization of colorectal mucus using infrared spectroscopy: a potential target for bowel cancer screening and diagnosis

Abstract

Biological materials presenting early signs of cancer would be beneficial for cancer screening/diagnosis. In this respect, the suitability of potentially exploiting mucus in colorectal cancer was tested using infrared spectroscopy in combination with statistical modeling. Twenty-six paraffinized colon tissue biopsy sections containing mucus regions from 20 individuals (10 normal and 16 cancerous) were measured using mid-infrared spectroscopic imaging. A digital de-paraffinization, followed by cluster analysis driven digital color-coded multi-staining segmented the infrared images into various histopathological features such as epithelium, connective tissue, stroma, and mucus regions within the tissue sections. Principal component analysis followed by supervised linear discriminant analysis was carried out on pure mucus and epithelial spectra from normal and cancerous regions of the tissue. For the mucus-based classification, a sensitivity of 96%, a specificity of 83%, and an area under the curve performance of 95% was obtained. For the epithelial tissue-based classification, a sensitivity of 72%, a specificity of 88%, and an area under the curve performance of 89% was obtained. The mucus spectral profiles further showed contributions indicative of glycans including that of sialic acid changes between these pathology groups. The study demonstrates that infrared spectroscopic analysis of mucus discriminates colorectal cancers with high sensitivity. This concept could be exploited to develop screening/diagnostic approaches complementary to histopathology.

Reflectance spectra analysis for mucous assessment

“…For cases in which there can be changes in the optical path length or hemoglobin concentration secondary to ischemia, spectral information that considers these expected changes and the corresponding optical interpretation is required. Although many kinds of mucosa modeling were reported, there are few studies of modeling of the spectral information of unique disease, and detailed research results must be conducted for practical application[ 48 - 50 ].…”

“…By contrast, the spectral analysis is expected for the accurate detection and objective diagnosis of diseases. The effectiveness of spectral data in the medical field has been addressed in previous reports[ 46 , 49 , 50 ]. In addition, the application of blood flow and hemoglobin mapping for implantation surgery using a small animal model was proposed[ 62 , 63 ].…”

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform–Infrared Spectroscopy Coupled with Machine Learning Algorithms

“…Fourier transform–infrared (FT–IR) spectroscopy can provide helpful information about the chemical structures and compositions of biological samples at the molecular level [ 15 ]. In the field of gastroenterology, several studies have been conducted on human and animal feces, serum, and colon biopsies using FT–IR and Raman spectroscopy for colitis screening, IBD and cancer diagnosis, and treatment efficacy monitoring [ 16 , 17 , 18 , 19 ]. However, to our knowledge, no published study has investigated prediction models for the efficacy of anti-TNF treatment in patients with IBD using fecal samples and FT–IR spectroscopy.…”

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning

“…46 Infrared spectroscopy has additionally shown potential for early disease diagnosis using blood samples 47,48 and mucus samples. 49 Recent developments in high-definition IR spectroscopic imaging 50 and discrete frequency infrared imaging 51,52 have made it possible to achieve high-quality images in clinically viable timescales. 37,[53][54][55][56] While studies have explored using spectral information to discriminate tumor grades, 57 most studies have utilized homogenous spatio-spectral regions.…”