17 February 2017 Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo
Author Affiliations +
Pancreatic cancer is the fourth leading cause of cancer death in the US. Currently, surgery is the only treatment that offers a chance of cure, however, accurately identifying tumor margins in real-time is difficult. Research has demonstrated that optical spectroscopy can be used to distinguish between healthy and diseased tissue. The design of a single-pixel imaging system for cancer detection is discussed. The system differentiates between healthy and diseased tissue based on differences in the optical reflectance spectra of these regions. In this study, pancreatic tissue samples from 6 patients undergoing Whipple procedures are imaged with the system (total number of tissue sample imaged was N=11). Regions of healthy and unhealthy tissue are determined based on SAM analysis of these spectral images. Hyperspectral imaging results are then compared to white light imaging and histological analysis. Cancerous regions were clearly visible in the hyperspectral images. Margins determined via spectral imaging were in good agreement with margins identified by histology, indicating that hyperspectral imaging system can differentiate between healthy and diseased tissue. After imaging the system was able to detect cancerous regions with a sensitivity of 74.50±5.89% and a specificity of 75.53±10.81%. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and assistance with cancer diagnosis and staging.
Conference Presentation
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Joseph Peller, Joseph Peller, Kyle J. Thompson, Kyle J. Thompson, Imran Siddiqui, Imran Siddiqui, John Martinie, John Martinie, David A. Iannitti, David A. Iannitti, Susan R. Trammell, Susan R. Trammell, } "Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo", Proc. SPIE 10060, Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 100600J (17 February 2017); doi: 10.1117/12.2250387; https://doi.org/10.1117/12.2250387

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