Identifying molecular contributors to autofluorescence of neoplastic and normal colon sections using excitation-scanning hyperspectral imaging.

Autofluorescence, the endogenous fluorescence present in cells and tissues, has historically been considered a nuisance in biomedical imaging. Many endogenous fluorophores, specifically, collagen, elastin, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide (FAD), are found throughout the human body. In fluorescence imaging scenarios, these signals can be prohibitive as they can outcompete signals introduced for diagnostic purposes. However, autofluorescence also contains information that has diagnostic value. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. We propose to utilize excitation-scanning hyperspectral imaging of autofluorescence to differentiate neoplastic lesions from surrounding non-neoplastic "normal" tissue. The spectra of isolated autofluorescent molecules are obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with an Xe arc lamp and thin-film tunable filter array (VersaChrome, Semrock, Inc.). Scans utilize excitation wavelengths from 360 to 550 nm in 5-nm increments. The resultant molecule-specific spectra are used to analyze hyperspectral image stacks from normal and neoplastic colorectal tissues. Due to a limited number of samples, neoplastic tissues examined here are a pool of both colorectal adenocarcinoma and adenomatous polyps. The hyperspectral images are analyzed with ENVI software and custom MATLAB scripts, including linear spectral unmixing. Initial results indicate the ability to separate signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states, in this case, normal colon versus neoplastic colon. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will focus on expanding the library of pure molecules, exploring histogram distance metrics as a means for identifying deviations in spectral signatures, and examining more defined disease states.

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning.

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.