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  • In vivo diagnosis of colonic precancer and cancer using near-infrared autofluorescence spectroscopy and biochemical modeling.

In vivo diagnosis of colonic precancer and cancer using near-infrared autofluorescence spectroscopy and biochemical modeling.

Journal of biomedical optics (2011-07-05)
Xiaozhuo Shao, Wei Zheng, Zhiwei Huang
ABSTRACT

The aim of this study is to evaluate the biochemical foundation and clinical capability of an image-guided near-infrared (NIR) autofluorescence (AF) spectroscopy technique for in vivo diagnosis of colonic malignancies during clinical colonoscopy. A novel endoscopic fiber-optic AF system was utilized for in vivo NIR AF measurements at 785 nm excitation. A total of 263 in vivo NIR AF spectra of colonic tissues were measured from 100 patients, in which 164 spectra were from benign tissue (116 normal and 48 hyperplastic polyps), 34 spectra were from precancer (adenomatous polyps), and 65 spectra were from cancer. The non-negativity constrained least squares minimization biochemical modeling was explored to estimate the biochemical compositions of colonic tissue using nine basis reference spectra from the representative biochemicals (i.e., collagen I, elastin, β-nicotinamide adenine dinucleotide, flavin adenine dinucleotide, L-tryptophan, hematoporphyrin, 4-pyridoxic acid, pyridoxal 5'-phosphate, and water) associated with structural or cellular metabolic progression in colonic precancer and cancer. High-quality in vivo NIR AF spectra in the spectral range of 810 to 1000 nm were acquired from colonic benign, precancerous, and cancerous mucosa under white-light reflectance endoscopic imaging guidance. Partial least squares discriminant analysis, together with the leave-one tissue site-out, cross validation on in vivo NIR AF spectra yields diagnostic sensitivities of 85.4%, 76.5%, and 84.6%, and specificities of 89.9%, 93.4%, and 91.4%, respectively, for classification of benign, precancer, and cancer in the colon. This work demonstrates that image-guided NIR AF spectroscopy in conjunction with biochemical modeling has promising potential for improving in vivo detection and diagnosis of colonic precancer and cancer during clinical colonoscopic screening.