The DOI matches its Crossref record; 41 of the 42 references it cites were found in a registry or an index.
Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks
Open accessAbstract
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 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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Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
Data-efficient and weakly supervised computational pathology on whole-slide images
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Artificial intelligence for multimodal data integration in oncology
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AI-based pathology predicts origins for cancers of unknown primary
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Metadata
- 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
- Published
- 2020-01
- Volume
- 26
- Issue
- 1
- Pages
- 52-58
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- 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
- 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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