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

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.

doi:10.1038/s41591-019-0715-9

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

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. +29 more·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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Several studies have shown potential for AI to facilitate intraoperative cancer diagnosis using label-free and cryosectioned tissue samples.
“several studies have demonstrated the potential of using AI to facilitate intraoperative diagnosis (e.g., during surgical resection) using label-free (40,41) and cryosectioned (42,43) tissue samples.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [41]

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INTELLIGENT INTRAOPERATIVE HISTOLOGY IN NEURO-ONCOLOGICAL SURGERY: STIMULATED RAMAN IMAGING, FOUNDATION MODELS, AND REAL-TIME MOLECULAR CLASSIFICATION
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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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Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

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