Supplementary Materials

Supplementary Material for:

Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy

Minbiao Ji, Spencer Lewis, Sandra Camelo-Piragua, Shakti H. Ramkissoon, Matija Snuderl, Sriram Venneti, Amanda Fisher-Hubbard, Mia Garrard, Dan Fu, Anthony C. Wang, Jason A. Heth, Cormac O. Maher, Nader Sanai, Timothy D. Johnson, Christian W. Freudiger, Oren Sagher, Xiaoliang Sunney Xie*, Daniel A. Orringer*

*Corresponding author. E-mail: dorringe{at}med.umich.edu (D.A.O.); xie{at}chemistry.harvard.edu (X.S.X.)

Published 14 October 2015, Sci. Transl. Med. 7, 309ra163 (2015)
DOI: 10.1126/scitranslmed.aab0195

This PDF file includes:

  • Methods
  • Fig. S1. SRS microscopy of pediatric medulloblastoma.
  • Fig. S2. SRS microscopy findings in a previously irradiated recurrent oligodendroglioma.
  • Fig. S3. SRS microscopy of minimally hypercellular gliomas.
  • Fig. S4. SRS and traditional microscopy of extrinsic brain tumors.
  • Fig. S5. SRS microscopy of spinal schwannoma.
  • Fig. S6. Validation of SRS image segmentation.
  • Fig. S7. Quantitative analysis of a normal specimen imaged with SRS microscopy.
  • Fig. S8. Quantification FOVs used to create the classifier.
  • Fig. S9. Planned workflow for ex vivo SRS-guided brain tumor resection.
  • Fig. S10. Planned workflow for in vivo SRS-guided brain tumor resection.
  • Table S1. Descriptive statistics of the test case series.
  • Table S2. In-depth verification of automated method for cellular density quantification.
  • Table S3. Test characteristics of independent biopsy parameters and the classifier as predictors of the presence of tumor infiltration.
  • Table S4. Comparison of pathologist and classifier performance on SRS microscopy survey.
  • Reference (46)

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