In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

• Type of Material: Article
• Authors: Martin Halicek, University of Texas Dallas; Himar Fabelo, University of Texas Dallas; Samuel Ortega, University of Las Palmas de Gran Canaria; Gustavo M. Callico, University of Las Palmas de Gran Canaria; Baowei Fei, Emory University
• Language: English
• Date: 2019-06-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers11060756
• Title of Journal or Parent Work: Cancers
• ISSN: 2072-6694
• Volume: 11
• Issue: 6
• Start Page: 756
• Grant/Funding Information: This research was supported in part by the U.S. National Institutes of Health (NIH) grants (R21CA176684, R01CA156775, R01CA204254, and R01HL140325).; In addition, this work has been supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperspectral Identification of Brain Tumors” under Grant Agreement ProID2017010164.; Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%)).; Moreover, this work has been supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria.
• Keywords: POSITIVE MARGINS; cancer; biomedical optical imaging; machine learning; COLON-CANCER; RESECTION; SQUAMOUS-CELL CARCINOMA; hyperspectral imaging; RANDOM FOREST; clinical diagnosis; Life Sciences & Biomedicine; BRAIN-TUMORS; Science & Technology; artificial intelligence; DETECTION ALGORITHMS; SPECTRAL-SPATIAL CLASSIFICATION; medical diagnostic imaging; Oncology; INTRAOPERATIVE ULTRASOUND; WAVELET ENTROPY
• Research Categories: Health Sciences, Oncology

Abstract

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.