A naturally brighter approach to colorectal cancer detection.

Abstract

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. The gold standard for diagnosis is tissue biopsy during colonoscopy and subsequent histopathology. Limitations of current techniques include the turnaround time required for histopathology and the limited ability to detect flat lesions due to inadequate contrast provided by traditional white light endoscopy (WLE). The focus of this work was to assess detection accuracy for differentiating CRC and noncancerous tissues using excitation-scanning hyperspectral imaging (Ex-HSI) of autofluorescence compared to current diagnostic methods. Fluorescence Ex-HSI permits detection of all emitted light above a cut-off wavelength. Ex-HSI has been shown to reduce acquisition time, improve signal-to-noise ratio, and increase spectral information compared to emission-scanning HSI. This study utilized a mouse CRC model in which Azoxymethane/Dextran sodium sulfate (AOM/DSS) treatments induced colitis with subsequent nodule formation. Ex-HSI images were validated using transmitted light images, confocal "z-stack" images, and histology sectioning with H&E staining. Ex-HSI images were corrected to a flat spectral response, and excitation spectra were extracted from selected regions within each field of view (FOV). Inflammation and rectal bleeding were observed in the initial 31-day timepoint consistent with the AOM/DSS treatment. Colorectal nodules were visible using 4x and 20x magnification objectives and confocal "z-stack" imaging. Extracted spectra displayed two to several peak excitation wavelengths, likely indicating the presence of multiple autofluorescent molecules. Further investigation will utilize principal component analysis (PCA) and convolutional neural networks (CNN) to assess detection performance.