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A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy

Authors :
Desroches, Joannie
Jermyn, Michael
Pinto, Michael
Picot, Fabien
Tremblay, Marie-Andrée
Obaid, Sami
Marple, Eric
Urmey, Kirk
Trudel, Dominique
Soulez, Gilles
Guiot, Marie-Christine
Wilson, Brian C.
Petrecca, Kevin
Leblond, Frédéric
Desroches, Joannie
Jermyn, Michael
Pinto, Michael
Picot, Fabien
Tremblay, Marie-Andrée
Obaid, Sami
Marple, Eric
Urmey, Kirk
Trudel, Dominique
Soulez, Gilles
Guiot, Marie-Christine
Wilson, Brian C.
Petrecca, Kevin
Leblond, Frédéric
Source :
PolyPublie
Publication Year :
2018

Abstract

Modern cancer diagnosis requires histological, molecular, and genomic tumor analyses. Tumor sampling is often achieved using a targeted needle biopsy approach. Targeting errors and cancer heterogeneity causing inaccurate sampling are important limitations of this blind technique leading to non-diagnostic or poor quality samples, and the need for repeated biopsies pose elevated patient risk. An optical technology that can analyze the molecular nature of the tissue prior to harvesting could improve cancer targeting and mitigate patient risk. Here we report on the design, development, and validation of an in situ intraoperative, label-free, cancer detection system based on high wavenumber Raman spectroscopy. This optical detection device was engineered into a commercially available biopsy system allowing tumor analysis prior to tissue harvesting without disrupting workflow. Using a dual validation approach we show that high wavenumber Raman spectroscopy can detect human dense cancer with >60% cancer cells in situ during surgery with a sensitivity and specificity of 80% and 90%, respectively. We also demonstrate for the first time the use of this system in a swine brain biopsy model. These studies set the stage for the clinical translation of this optical molecular imaging method for high yield and safe targeted biopsy.

Details

Database :
OAIster
Journal :
PolyPublie
Notes :
PolyPublie
Publication Type :
Electronic Resource
Accession number :
edsoai.on1429911748
Document Type :
Electronic Resource