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Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Open access
Kong, Kenny, Rowlands, Christopher J., Varma, Sandeep, Perkins, William, Leach, Iain H., Koloydenko, Alexey A., Williams, Hywel C., Notingher, Ioan·2013·Proceedings of the National Academy of Sciences

Abstract

Significance: Histopathology is the standard method for diagnosis of cancer. However, this method requires time-consuming procedures for sectioning and staining of tissues, making histopathology impractical for use during surgery for most cancer types. We report a unique method based on two optical spectroscopy techniques—autofluorescence imaging and Raman scattering—that can accurately measure molecular differences between tumor cells and healthy tissue and allows diagnosis of tumors faster than histopathology, without requiring tissue sectioning or staining. Our study demonstrates the potential of this technique for diagnosis of tissues during cancer surgery, providing a quick and objective way to determine whether the tissue layers removed by the surgeon are clear of tumor.

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Metadata

Authors
Kong, K., Rowlands, C. J., Varma, S., Perkins, W., Leach, I. H., Koloydenko, A. A., Williams, H. C., & Notingher, I.
Venue
Proceedings of the National Academy of Sciences
Published
2013-09-03
Volume
110
Issue
38
Pages
15189-15194
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2163932563
Cited by
234
References
0
FWCI
11.58
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy. https://app.cochat.ai/papers/10.1073/pnas.1311289110
The paper
Kong, K., Rowlands, C. J., Varma, S., Perkins, W., Leach, I. H., Koloydenko, A. A., Williams, H. C., & Notingher, I. (2013). Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1311289110

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.