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Wallace, M.B., Perelman, L.T., Backman, V., Crawford, J.M., Fitzmaurice, M., Seiler, M., Badizadegan, K., Shields, S.J., Itzkan, I. and Dasari, R.R. (2000) Endoscopic Detection of Dysplasia in Patients with Barrett’s Esophagus Using Light-Scattering Spectroscopy. Gastroenterology, 119, 677-682.
https://doi.org/10.1053/gast.2000.16511

has been cited by the following article:

• TITLE: Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue ex Vivo, a Pilot Study

  AUTHORS: Joseph Peller, Cobey L. McGinnis, Kyle J. Thompson, Imran Siddiqui, John Martinie, David A. Iannitti, Susan R. Trammell

  KEYWORDS: Hyperspectral Imaging, Cancer Detection, Reflectance Spectroscopy, Single Pixel Camera

  JOURNAL NAME: Open Journal of Medical Imaging, Vol.11 No.4, December 7, 2021

  ABSTRACT: Cancer is the second-leading cause of death in the United State and surgery remains the primary treatment for most solid mass tumors. However, accurately identifying tumor margins in real-time remains a challenge. In this study, the design and testing of hyperspectral imaging (HSI) system based on a single-pixel camera engine is discussed. The primary advantage of a single pixel architecture over traditional scanning HSI techniques is its high sensitivity and potential to function at low light levels. The objective for the imaging system described here is to detect changes in the reflectance spectra of tissue and to use these differences to delineate tumor margins. This paper presents the results of a 19-patient pilot study that assesses the ability of the HSI system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo. Pancreatic tissue excised during pancreatectomy was imaged immediately after being sent to the pathology lab. A pathologist sectioned the tissue and placed samples into standard tissue embedding cassettes. These tissue samples were then imaged using the HSI system. After imaging, the samples were returned to the pathologist for processing and analysis. The HSI was later compared to the histological analysis. The spectral angle mapping (SAM) and support vector machine (SVM) algorithms were used to classify pixels in the HSI images as healthy or unhealthy in order to delineate margins. Good agreement between margins determined via HSI (using both SAM and SVM) and histology/white light imaging was found.