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CNS cancer

Intraoperative brain tumour identification with deep learning

Developing novel technologies to discriminate malignant tissue from nonmalignant structures and thereby facilitate safe, complete tumour resection is a major priority for advancing oncological neurosurgery. Herein, we discuss a recently reported innovation involving stimulated Raman spectroscopy of intraoperative tissue samples and data interpretation with artificial intelligence, as well as the implications of this approach for neurosurgical oncology.

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References

  1. Lara-Velazquez, M. et al. Advances in brain tumor surgery for glioblastoma in adults. Brain Sci. 7, 166 (2017).

    Article  Google Scholar 

  2. Sanai, N. & Berger, M. S. Surgical oncology for gliomas: the state of the art. Nat. Rev. Clin. Oncol. 15, 112–125 (2018).

    Article  Google Scholar 

  3. Ji, M. et al. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy. Sci. Transl Med. 7, 309ra163 (2015).

    Article  Google Scholar 

  4. Ji, M. et al. Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy. Sci. Transl Med. 5, 201ra119 (2013).

    Article  Google Scholar 

  5. Orringer, D. A. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-016-0027 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Menze, B. H. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imag. 34, 1993–2024 (2015).

    Article  Google Scholar 

  7. Gulshan, V. et al. Performance of a deep-learning algorithm versus manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmol. https://doi.org/10.1001/jamaophthalmol.2019.2004 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).

    Article  CAS  Google Scholar 

  9. Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26, 52–58 (2020).

    Article  CAS  Google Scholar 

  10. Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? arXiv https://arxiv.org/abs/1411.1792 (2014).

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Correspondence to Eric K. Oermann.

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Martini, M.L., Oermann, E.K. Intraoperative brain tumour identification with deep learning. Nat Rev Clin Oncol 17, 200–201 (2020). https://doi.org/10.1038/s41571-020-0343-9

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