Cited by 790Spectroscopy Techniques in Biomedical and Chemical ResearchAI in cancer detectionOptical Imaging and Spectroscopy Techniques

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Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

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Hollon, Todd C., Pandian, Balaji, Adapa, Arjun R., Urias, Esteban, Save, Akshay V., Khalsa, Siri Sahib S., Eichberg, Daniel G., D’Amico, Randy S., Farooq, Zia U., Lewis, Spencer, Petridis, Petros D., Marie, Tamara, Shah, Ashish H., Garton, Hugh J. L., Maher, Cormac O., Heth, Jason A., McKean, Erin L., Sullivan, Stephen E., Hervey-Jumper, Shawn L., Patil, Parag G., Thompson, B. Gregory, Sagher, Oren, McKhann, Guy M., Komotar, Ricardo J., Ivan, Michael E., Snuderl, Matija, Otten, Marc L., Johnson, Timothy D., Sisti, Michael B., Bruce, Jeffrey N., Muraszko, Karin M., Trautman, Jay, Freudiger, Christian W., Canoll, Peter, Lee, Honglak, Camelo-Piragua, Sandra, Orringer, Daniel A.·2020·Nature Medicine

DOI 10.1038/s41591-019-0715-9PMC PMC6960329

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Abstract

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive 2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce 4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

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Ostrom, Q. T., Gittleman, H. R., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., Stroup, N. E., Kruchko, C., & Barnholtz‐Sloan, J. S. (2013) · Neuro-Oncology · doi:10.1093/neuonc/not151 · Cited by 12,063
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Science of Science: An Integrated and Methods-Driven Understanding of Science
Krauß, A. (2024) · Oxford University Press eBooks · doi:10.1093/9780198937401.003.0017 · Cited by 1
Congress of Neurological Surgeons systematic review and evidence based guideline on neuropathology for WHO grade II diffuse glioma: update
Mandelberg, N. J., Hodges, T. R., Wang, T. J. C., McGranahan, T. M., Olson, J. J., & Orringer, D. A. (2025) · Journal of Neuro-Oncology · doi:10.1007/s11060-024-04898-7 · Cited by 1
Self-Calibrated Stimulated Raman Scattering Spectroscopy for Rapid Cholangiocarcinoma Diagnosis
Guo, J., Zhang, L., Yu, Q., Qi, Y., Zhang, H., Zhang, L., Yuan, C., Li, M., & Xiong, H. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c00480 · Cited by 1
Spectroscopic Analysis of the Extracellular Matrix in Naked Mole-Rat Temporomandibular Joints
Adachi, T., Imamura, H., Yaji, T., Mochizuki, K., Zhu, W., Shindo, S., Shibata, S., Adachi, K., Yamamoto, T., Oseko, F., et al. (2025) · Gels · doi:10.3390/gels11060414 · Cited by 1
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Liu, J., Liu, R., Zhang, X., Chen, X. G., & Yue, S. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500072 · Cited by 1
Label-free fluorescence lifetime imaging for real-time guidance of stereotactic biopsy: A feasibility study in brain cancer patients
Garcia, A. A., Bec, J., Hassan, M. A., Anbunesan, S. N., Kraft, L., Bobinski, M., Jin, L., Lee, H. S., Harsh, G. R., Bloch, O. G., et al. (2025) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaf172 · Cited by 1
Influence of preprocessing of stimulated Raman scattering images on the performance of deep neural networks for detecting cancer tissue
Weber, A., Enderle-Ammour, K., Schröder, K.-M., Metzger, M. C., Brandenburg, L. S., Beck, J., Straehle, J., Steybe, D., Hassan, M. K., Schmid, S., et al. (2025) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-2024-2608 · Cited by 1
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Al‐Shaebi, Z., Akdeniz, M., & Aydin, Ö. F. (2025) · Springer eBooks · doi:10.1007/978-3-031-99410-4_27 · Cited by 1
Protocol to annotate and automate single-cell instance segmentation on stimulated Raman histology using deep learning
Bhattacharya, A., Landgraf, E., Jiang, C., Chowdury, A., Kondepudi, A., Wang, L., Harake, E. S., Hou, X., Walsh, L., & Hollon, T. C. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.104221 · Cited by 1
Miniaturized coherent anti-stokes Raman scattering (Mini-CARS) microscopy for intraoperative peritoneal lavage cytology in gastric cancer
Wu, F., Chen, X., Jiang, Y., Wu, Z., KangWen, Y. A. N. G., Xia, Y., Li, R., Lan, L., Hong, W., Yue, S., et al. (2025) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545825500397 · Cited by 1
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Hashimi, S. H., & Azizi, N. M. (2025) · Journal of Natural Science Review · doi:10.62810/jnsr.v3i4.220 · Cited by 1
AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation
Yu, Y., Guo, Y., Luo, K., & Ji, M. (2026) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.5c00199 · Cited by 1
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Fatehi, M., Tosefsky, K. N., Makarenko, S., Martin, K. C., Harrison, R. A., Müller, F.-J., Spence, T., Gooderham, P. A., & Yip, S. S. F. (2026) · Trends in cancer · doi:10.1016/j.trecan.2025.12.003 · Cited by 1
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Costa, P. C., Bharadwaj, S., Li, Z., Kaza, N., López-Esteva, M., Lien, A. D., Haider, B., & Robles, F. E. (2026) · npj Imaging · doi:10.1038/s44303-026-00147-w · Cited by 1
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Rajput, S., Khandia, R., Gaur, T., Anant, A., Kamal, M., Gurjar, P., Al-Hussain, S. A., & Zaki, M. E. A. (2026) · Discover Nano · doi:10.1186/s11671-026-04478-w · Cited by 1
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Alvarado, E., Li, Z., Li, Y., & Shi, L. (2026) · PhotoniX Life · doi:10.3724/pxlife.2025-0016 · Cited by 1
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Jawar, S., Tiwari, S., & Kashyap, R. (2026) · Innovation and Emerging Technologies · doi:10.1142/s2737599426400050 · Cited by 1
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Assadi, M., Juweid, M. E., Lohmann, P., Minniti, G., Sahm, F., Ivanidze, J., Karschnia, P., Landry, A., Mottaghy, F. M., & Zadeh, G. M. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01148-9 · Cited by 1
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Byun, Y. H., Seo, H., Won, J., Lee, B. S., Hong, D. H., Nam, S. M., Hwang, J. H., Kim, M.-S., Kim, Y.-H., Kim, J., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02651-0 · Cited by 1
Stimulated Raman scattering microscopy: fundamentals, instrumentation, and chemical imaging applications
吕发科 (2026) · Frontiers in Chemical Biology · doi:10.3389/fchbi.2026.1754307 · Cited by 2
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Zhang, G., zhao, C., Yang, Q., Fang, Y., Sun, X., Guo, Y., Hu, R., Shen, B., & Liu, L. (2026) · iOptics · doi:10.1016/j.iopt.2026.100032 · Cited by 1
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Fujioka, H., & Kamiya, M. (2026) · JACS Au · doi:10.1021/jacsau.6c00290 · Cited by 2
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Qasem, M. H., Sariera, T. M. A., Alhumaid, K. F., Alshraah, S. M., Mufleh, A. S. S., & Chihaoui, N. (2026) · Journal of Imaging · doi:10.3390/jimaging12060228 · Cited by 1
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Reddy, A. M., Kaur, G., Ribaya, V. S. D., Ribaya, E. L. A., Reddy, M. C., Shah, T., Shinde, D. A., & Suri, G. S. (2026) · Discover Oncology · doi:10.1007/s12672-026-05242-3 · Cited by 1
Coherent nonlinear vibrational microscopy
Luo, K., Hu, D., Li, J., Yu, Y., Xue, K., Ji, Y., Yang, F., Ao, J., & Ji, M. (2026) · Applied Physics Reviews · doi:10.1063/5.0313530 · Cited by 1
Automatic Coregistration of High-Resolution MALDI-MSI and Raman Imaging Applied to Cardiac Tissue of Fabry Disease Mouse Models
Dierks, J., Brockmann, E. U., Arias-Loza, P.-A., Bocklitz, T. W., Nordbeck, P. N., Lorenz, K., Tolstik, E., & Heiles, S. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c07622 · Cited by 1
Frozen agarose–assisted tissue preparation for stimulated Raman histology enables rapid intraoperative assessment of peripheral nerve integrity
Schöffing, F., Hohenhaus, M., Beck, J., Scholz, C., & Straehle, J. (2026) · Brain and Spine · doi:10.1016/j.bas.2026.106174 · Cited by 1

Authors: Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., Petridis, P. D., Marie, T., Shah, A. H., Garton, H. J. L., Maher, C. O., Heth, J. A., McKean, E. L., Sullivan, S. E., Hervey-Jumper, S. L., Patil, P. G., Thompson, B. G., Sagher, O., McKhann, G. M., Komotar, R. J., Ivan, M. E., Snuderl, M., Otten, M. L., Johnson, T. D., Sisti, M. B., Bruce, J. N., Muraszko, K. M., Trautman, J., Freudiger, C. W., Canoll, P., Lee, H., Camelo-Piragua, S., & Orringer, D. A.
Affiliations: University of Michigan (US)
Columbia University (US)
University of Miami (US)
Invenio Imaging (United States) (US)
New York University (US)
Corresponding author: Orringer, Daniel A.
ORCID: Hollon, Todd C.
Pandian, Balaji
Adapa, Arjun R.
Save, Akshay V.
Khalsa, Siri Sahib S.
Eichberg, Daniel G.
D’Amico, Randy S.
Farooq, Zia U.
Petridis, Petros D.
Shah, Ashish H.
Garton, Hugh J. L.
Maher, Cormac O.
Heth, Jason A.
McKean, Erin L.
Patil, Parag G.
Thompson, B. Gregory
Sagher, Oren
McKhann, Guy M.
Komotar, Ricardo J.
Ivan, Michael E.
Snuderl, Matija
Johnson, Timothy D.
Sisti, Michael B.
Bruce, Jeffrey N.
Muraszko, Karin M.
Canoll, Peter
Lee, Honglak
Camelo-Piragua, Sandra
Orringer, Daniel A.
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2020-01
Volume: 26
Issue: 1
Pages: 52-58
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-019-0715-9
PMC: PMC6960329
OpenAlex: W2998401461
Cited by: 790
References: 42
Citations by year: 2026: 102 · 2025: 134 · 2024: 147 · 2023: 113 · 2022: 115 · 2021: 125 · 2020: 53
FWCI: 48.28
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Medicine (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01CA226527-02)
Neurosurgery Research and Education Foundation
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
Keywords: brain tumors, intraoperative diagnosis, stimulated Raman histology, deep convolutional neural networks, label-free optical imaging, semantic segmentation, artificial intelligence in pathology
MeSH terms: Deep Learning, Algorithms, Brain Neoplasms, Clinical Trials as Topic, Computer Systems, Humans, Image Processing, Computer-Assisted, Probability, Spectrum Analysis, Raman, Monitoring, Intraoperative, Neural Networks, Computer

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