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Nanoparticle-Enabled Optical Endoscopy: Extending the Frontiers of Diagnosis and Treatment

  • Brian C. WilsonEmail author
  • Santa Borel
Chapter
Part of the Progress in Optical Science and Photonics book series (POSP, volume 3)

Abstract

Endoscopy—looking deep inside the body with light—is an important part of standard medical practice, for disease detection/localization and staging and to guide treatments and monitor responses. This is especially the case in oncology applications, which is the primary focus of this chapter. However, established endoscopy techniques are unable to meet all the clinical needs and in particular fail to exploit the rich information provided by advances in molecular biology, including genomics and proteomics. Incorporating the use of nanoparticles into endoscopic technologies and procedures can significantly extend their capabilities and hence potential clinical impact. This chapter describes the endoscopic techniques that are currently in use, as well as emerging approaches using different light-tissue interactions, and how incorporating nanoparticles can enhance their information content and hence clinical sensitivity and specificity. Specific examples of current research in this field are presented in more detail to demonstrate the range of potential nanoparticle applications. Thus, surface enhanced Raman scattering nanoparticles are being developed to achieve biomarker-targeted, multiplexed imaging for tissue characterization by endoscopy, while lipid-porphyrin nanoparticles can be conjugated to targeting agents and visualized through high red/near-infrared absorption using photoacoustic methods as well as being used to enhance and spatially-localize photothermal treatment. Optimal nanoparticles for photodynamic therapy are also discussed. Challenges in the translation into clinical practice of emerging nanoparticle-enabled endoscopies are highlighted.

Keywords

Optical Coherence Tomography Surface Enhance Raman Scattering Endoscopic Imaging Reporter Molecule Intrinsic Optical Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.University of Toronto/University Health NetworkTorontoCanada

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