Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

Silas J. Leavesley; Joshua Deal; Shante Hill; Will A. Martin; Malvika Lall; Carmen Lopez; Paul F. Rider; Thomas C. Rich; Carole W. Boudreaux

doi:10.1117/12.2290696

19 February 2018 Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

Silas J. Leavesley, Joshua Deal, Shante Hill, Will A. Martin, Malvika Lall, Carmen Lopez, Paul F. Rider, Thomas C. Rich, Carole W. Boudreaux

Author Affiliations +

Proceedings Volume 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis; 104890K (2018) https://doi.org/10.1117/12.2290696
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

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.

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Silas J. Leavesley, Joshua Deal, Shante Hill, Will A. Martin, Malvika Lall, Carmen Lopez, Paul F. Rider, Thomas C. Rich, and Carole W. Boudreaux "Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning", Proc. SPIE 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 104890K (19 February 2018); https://doi.org/10.1117/12.2290696

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