Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
08
08
2019
accepted:
24
11
2019
pubmed:
8
1
2020
medline:
14
4
2020
entrez:
8
1
2020
Statut:
ppublish
Résumé
Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery
Identifiants
pubmed: 31907460
doi: 10.1038/s41591-019-0715-9
pii: 10.1038/s41591-019-0715-9
pmc: PMC6960329
mid: NIHMS1544390
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
52-58Subventions
Organisme : NINDS NIH HHS
ID : K08 NS110919
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA226527
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
Sullivan, R. et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 16, 1193–1224 (2015).
doi: 10.1016/S1470-2045(15)00223-5
Novis, D. A. & Zarbo, R. J. Interinstitutional comparison of frozen section turnaround time. A College of American Pathologists Q-Probes study of 32868 frozen sections in 700 hospitals. Arch. Pathol. Lab. Med. 121, 559–567 (1997).
Gal, A. A. & Cagle, P. T. The 100-year anniversary of the description of the frozen section procedure. JAMA 294, 3135–3137 (2005).
doi: 10.1001/jama.294.24.3135
Robboy, S. J. et al. Pathologist workforce in the United States: I. Development of a predictive model to examine factors influencing supply. Arch. Pathol. Lab. Med. 137, 1723–1732 (2013).
doi: 10.5858/arpa.2013-0200-OA
Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).
doi: 10.1126/science.1165758
Orringer, D. A. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. 1, ii (2017).
Ji, M. et al. Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy. Sci. Transl. Med. 5, 201ra119 (2013).
doi: 10.1126/scitranslmed.3005954
Top 100 Lab Procedures Ranked by Service (2017); https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareFeeforSvcPartsAB/Downloads/LabCHARG17.pdf?agree=yes&next=Accept
Metter, D. M., Colgan, T. J., Leung, S. T., Timmons, C. F. & Park, J. Y. Trends in the US and Canadian Pathologist Workforces From 2007 to 2017. JAMA Netw. Open 2, e194337 (2019).
doi: 10.1001/jamanetworkopen.2019.4337
Hollon, T. C. et al. Rapid intraoperative diagnosis of pediatric brain tumors using stimulated Raman histology. Cancer Res. 78, 278–289 (2018).
doi: 10.1158/0008-5472.CAN-17-1974
Louis, D. N. et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131, 803–820 (2016).
doi: 10.1007/s00401-016-1545-1
Ji, M. et al. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy. Sci. Transl. Med. 7, 309ra163 (2015).
doi: 10.1126/scitranslmed.aab0195
Krizhevsky, A. et al. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, 1097–1105 (Curran Associates, Inc., 2012).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
doi: 10.1001/jama.2016.17216
Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).
doi: 10.1038/s41591-018-0147-y
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
doi: 10.1038/nature21056
Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).
doi: 10.1038/srep26286
Coudray, N. et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).
doi: 10.1038/s41591-018-0177-5
He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. Proc. 2015 IEEE International Conf. Computer Vision (ICCV) 1026–1034 (IEEE Computer Society, 2015).
Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. A. Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI 4, 12 (2017).
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
doi: 10.1038/s41591-018-0300-7
Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology 19, v1–v88 (2017).
doi: 10.1093/neuonc/nox158
Lee, K., Lee, K., Lee, H. & Shin, J. A Simple Unified Framework for Detecting Out-of-distribution Samples and Adversarial Attacks. Proc. 32nd International Conference on Neural Information Processing Systems 7167–7177 (2018).
Erhan, D, Bengio, Y, Courville, A. & Vincent, P. Visualizing Higher-Layer Features of a Deep Network. Technical Report, Univeristé de Montréal (2009).
Lu, F.-K. et al. Label-free neurosurgical pathology with stimulated Raman imaging. Cancer Res. 76, 3451–3462 (2016).
doi: 10.1158/0008-5472.CAN-16-0270
Kohe, S., Colmenero, I., McConville, C. & Peet, A. Immunohistochemical staining of lipid droplets with adipophilin in paraffin-embedded glioma tissue identifies an association between lipid droplets and tumour grade. J. Histol. Histopathol. 4, 4 (2017).
doi: 10.7243/2055-091X-4-4
Chen, P.-H. C. et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat. Med. 25, 1453–1457 (2019).
doi: 10.1038/s41591-019-0539-7
Viola, K. V. et al. Mohs micrographic surgery and surgical excision for nonmelanoma skin cancer treatment in the Medicare population. Arch. Dermatol. 148, 473–477 (2012).
doi: 10.1001/archdermatol.2011.2456
Hoesli, R. C., Orringer, D. A., McHugh, J. B. & Spector, M. E. Coherent Raman scattering microscopy for evaluation of head and neck carcinoma. Otolaryngol. Head Neck Surg. 157, 448–453 (2017).
doi: 10.1177/0194599817700388
Carter, C. L., Allen, C. & Henson, D. E. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63, 181–187 (1989).
doi: 10.1002/1097-0142(19890101)63:1<181::AID-CNCR2820630129>3.0.CO;2-H
Ratnavelu, N. D. G. et al. Intraoperative frozen section analysis for the diagnosis of early stage ovarian cancer in suspicious pelvic masses. Cochrane Database Syst. Rev. 3, CD010360 (2016).
Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).
doi: 10.1073/pnas.1219747110
Dammers, R. et al. Towards improving the safety and diagnostic yield of stereotactic biopsy in a single centre. Acta Neurochir. 152, 1915–1921 (2010).
doi: 10.1007/s00701-010-0752-0
Zeiler, M. D. & Fergus, R. Visualizing and Understanding Convolutional Networks. Computer Vision – ECCV 2014 818–833 (2014).
Freudiger, C. W. et al. Stimulated Raman scattering microscopy with a robust fibre laser source. Nat. Photonics 8, 153–159 (2014).
doi: 10.1038/nphoton.2013.360
Liu, Y. et al. Detecting cancer metastases on gigapixel pathology images. arXiv [cs.CV] (2017). https://arxiv.org/abs/1703.02442
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? Proc. 27th International Conference on Neural Information Processing Systems 2, 3320–3328 (2014).
Abadi, M. et al. Tensorflow: a system for large-scale machine learning. OSDI 16, 265–283 (2016).
Hou, L. et al. Patch-based convolutional neural network for whole slide tissue image classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2424–2433 (2016).
Qin, Z. et al. How convolutional neural networks see the world: a survey of convolutional neural network visualization methods. Math. Found. Comput. 1, 149–180 (2018).
doi: 10.3934/mfc.2018008