First experience in clinical application of hyperspectral endoscopy for evaluation of colonic polyps

• Published in: Journal of biophotonics 2021-09, Vol.14 (9), p.e202100078-n/a
• Main Authors: Yoon, Jonghee, Joseph, James, Waterhouse, Dale J., Borzy, Charlie, Siemens, Kyla, Diamond, Sarah, Tsikitis, Vassiliki Liana, Bohndiek, Sarah E.
• Format: Article
• Language: English
• Subjects: Adenomatous Polyps - diagnostic imaging; Adenomatous Polyps - surgery; Cancer; Colonic Polyps - diagnostic imaging; Colonic Polyps - surgery; Colonoscopy; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnostic imaging; Data acquisition; Early Detection of Cancer; Endoscopes; Endoscopy; Humans; hyperspectral endoscope; hyperspectral imaging; Learning algorithms; Machine learning; Patients; Polyps; Spectra
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.202100078

Early detection and resection of adenomatous polyps prevents their progression to colorectal cancer (CRC), significantly improving patient outcomes. Polyps are typically identified and removed during white‐light colonoscopy. Unfortunately, the rate of interval cancers that arise between CRC screening events remains high, linked to poor visualization of polyps during screening and incomplete polyp removal. Here, we sought to evaluate the potential of a hyperspectral endoscope (HySE) to enhance polyp discrimination for detection and resection. We designed, built and tested a new compact HySE in a proof‐of‐concept clinical study. We successfully collected spectra from three tissue types in seven patients undergoing routine colonoscopy screening. The acquired spectral data from normal tissue and polyps, both pre‐ and post‐ resection, were subjected to quantitative analysis using spectral angle mapping and machine learning, which discriminated the data by tissue type, meriting further investigation of HySE as a clinical tool. A new hyperspectral endoscope (HySE) was designed to enhance polyp discrimination for detection and resection. Spectra from three tissue types in seven patients undergoing routine colonoscopy screening were collected. The acquired spectral data from normal tissue and polyps, both pre and post resection, were subjected to quantitative analysis using spectral angle mapping and machine learning, which discriminated the data by tissue type, meriting further investigation of HySE as a clinical tool.