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

• Published in: Nature biomedical engineering Vol. 1; no. 2
• Main Authors: Orringer, Daniel A, Pandian, Balaji, Niknafs, Yashar S, Hollon, Todd C, Boyle, Julianne, Lewis, Spencer, Garrard, Mia, Hervey-Jumper, Shawn L, Garton, Hugh J L, Maher, Cormac O, Heth, Jason A, Sagher, Oren, Wilkinson, D Andrew, Snuderl, Matija, Venneti, Sriram, Ramkissoon, Shakti H, McFadden, Kathryn A, Fisher-Hubbard, Amanda, Lieberman, Andrew P, Johnson, Timothy D, Xie, X Sunney, Trautman, Jay K, Freudiger, Christian W, Camelo-Piragua, Sandra
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.01.2017
• Subjects: Brain; Brain tumors; Decision making; Diagnosis; Diagnostic systems; Fiber lasers; Histology; Human tissues; Image processing; Laser applications; Lasers; Medical imaging; Metastases; Microscopy; Multilayer perceptrons; Neuroimaging; Neurosurgery; Raman spectra; Raman spectroscopy; Surgery; Tumors
• ISSN: 2157-846X; 2157-846X
• DOI: 10.1038/s41551-016-0027

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 by 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) - which 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.