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

Nature Biomedical Engineering volume 1, Article number: 0027 (2017) Cite this article

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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.

The optimal surgical management of brain tumours varies widely depending on histologic subtype. Although some tumours of the central nervous system (CNS) have a distinct gross appearance, others are difficult to differentiate. Consequently, the importance of intraoperative histopathologic diagnosis in brain-tumour surgery has been recognized for over 85 years1.

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Figure 1: Engineering a clinical SRS microscope.
Figure 2: Creating virtual H&E slides with the clinical SRS microscope.
Figure 3: Imaging of key diagnostic histoarchitectural features with SRH.
Figure 4: SRH reveals structural heterogeneity in human brain tumours.
Figure 5: Simulation of intraoperative histologic diagnosis with SRH.
Figure 6: MLP classification of SRH images.
Figure 7: MLP-based diagnostic prediction.

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Acknowledgements

The authors would like to thank H. Wagner for manuscript editing. Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering (R01EB017254 to X.S.X. and D.A.O.), National Institute of Neurologic Disorders and Stroke (K08NS087118 to S.H.R.), the National Institutes of Health Director’s Transformative Research Award Program T-R01 (R01EB010244-01 to X.S.X.) and the National Cancer Institute of the National Institutes of Health (P30CA046592). This work was also supported by Fast Forward Medical Innovation, the University of Michigan—Michigan Translational Research and Commercialization for Life Sciences Program (U-M MTRAC) and the Michigan Institute for Clinical and Health Research (2UL1TR000433).

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Authors and Affiliations

  1. Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    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

  2. Department of Pathology, New York University, New York, 10016, New York, USA

    Matija Snuderl

  3. Department of Neurology, New York University, New York, 10016, New York, USA

    Matija Snuderl

  4. Department of Pathology, Section of Neuropathology, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Sriram Venneti, Kathryn A. McFadden, Amanda Fisher-Hubbard, Andrew P. Lieberman & Sandra Camelo-Piragua

  5. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Shakti H. Ramkissoon

  6. Department of Medical Oncology, Center for Molecular Oncologic Pathology, Dana Farber Cancer Institute, Boston, 02115, Massachusetts, USA

    Shakti H. Ramkissoon

  7. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Timothy D. Johnson

  8. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, 02138, Massachusetts, USA

    X. Sunney Xie

  9. Invenio Imaging Inc., Santa Clara, 95051, California, USA

    Jay K. Trautman & Christian W. Freudiger

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Contributions

D.A.O., B.P., Y.S.N., C.W.F., J.K.T., T.C.H. and S.C.-P. conceived the study, designed the experiments and wrote the paper; they were assisted by M.G. and X.S.X., who provided guidance on the study design. D.A.O., S.L. and M.G. performed the SRH imaging of all specimens. C.W.F. and J.K.T. built the SRS microscope. B.P., Y.S.N., J.B. and T.D.J. analysed the data. S.C.-P., K.A.M., S.H.R., M.S., S.V., A.P.L. and A.F.-H. interpreted microscopic images and revised the manuscript. T.D.J., D.A.W. and Y.S.N. performed the statistical analyses. D.A.O., S.L.H.-J., H.J.L.G., J.A.H., C.O.M. and O.S. provided surgical specimens for imaging. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Daniel A. Orringer or Sandra Camelo-Piragua.

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Competing interests

X.S.X. and D.A.O. are advisers and shareholders of Invenio Imaging, Inc., a company developing SRS microscopy systems. C.W.F. and J.K.T. are employees and shareholders of Invenio Imaging, Inc.

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Orringer, D., Pandian, B., Niknafs, Y. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat Biomed Eng 1, 0027 (2017). https://doi.org/10.1038/s41551-016-0027

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