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

  • Daniel A. Orringer
  • , Balaji Pandian
  • , Yashar S. Niknafs
  • , Todd C. Hollon
  • , Julianne Boyle
  • , Spencer Lewis
  • , Mia Garrard
  • , Shawn L. Hervey-Jumper
  • , Hugh J.L. Garton
  • , Cormac O. Maher
  • , Jason A. Heth
  • , Oren Sagher
  • , D. Andrew Wilkinson
  • , Matija Snuderl
  • , Sriram Venneti
  • , Shakti H. Ramkissoon
  • , Kathryn A. McFadden
  • , Amanda Fisher-Hubbard
  • , Andrew P. Lieberman
  • , Timothy D. Johnson
  • X. Sunney Xie, Jay K. Trautman, Christian W. Freudiger, Sandra Camelo-Piragua

Research output: Contribution to journalArticlepeer-review

Abstract

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method - stimulated Raman histology (SRH) - that leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain-tumour patients.

Original languageEnglish (US)
Article number0027
JournalNature Biomedical Engineering
Volume1
Issue number2
DOIs
StatePublished - Feb 10 2017

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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