Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy

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Author
Orringer, Daniel
Pandian, Balaji
Niknafs, Yashar
Hollon, Todd
Boyle, Julianne
Lewis, Spencer
Garrard, Mia
Hervey-Jumper, Shawn
Garton, Hugh
Maher, Cormac
Heth, Jason
Sagher, Oren
Wilkinson, D.
Snuderl, Matija
Venneti, Sriram
McFadden, Kathryn
Fisher-Hubbard, Amanda
Lieberman, Andrew
Johnson, Timothy
Trautman, Jay
Freudiger, Christian
Camelo-Piragua, Sandra
Note: Order does not necessarily reflect citation order of authors.
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https://doi.org/10.1038/s41551-016-0027Metadata
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Orringer, Daniel A., Balaji Pandian, Yashar S. Niknafs, Todd C. Hollon, Julianne Boyle, Spencer Lewis, Mia Garrard, et al. 2017. “Rapid Intraoperative Histology of Unprocessed Surgical Specimens via Fibre-Laser-Based Stimulated Raman Scattering Microscopy.” Nature Biomedical Engineering 1 (2) (February 6): 0027. doi:10.1038/s41551-016-0027.Abstract
Conventional methods for intraoperative histopathologic diagnosis are labor- and time-intensive and may delay decision-making during brain tumor surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain tumor infiltration in fresh, unprocessed human tissues. Previously, the execution of SRS microscopy in a clinical setting has not been possible. We report the first demonstration of SRS microscopy in an operating room using a portable fiber-laser-based microscope in unprocessed specimens from 101 neurosurgical patients. Additionally, we introduce an image-processing method, stimulated Raman histology (SRH), which leverages SRS images to create virtual hematoxylin and eosin- stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, the concordance of SRH and conventional histology for predicting diagnosis was nearly perfect (κ>0.89) and accuracy exceeded 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain tumor subtype with 90% accuracy. This study provides insight into how SRH can now be used to improve the surgical care of brain tumor patients.Terms of Use
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:33471123
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