We used deep neural networks trained on optical histology and open-source genomic data to predict the molecular genetics of brain tumors during surgery. Our results represent how AI-based diagnostics can provide a valuable adjunct to wet laboratory methods for molecular testing in patients with cancer.
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References
Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro. Oncol. 21, 1498–1508 (2021). This paper summarizes the updated WHO tumor classification with full integration of molecular features for brain tumor diagnosis.
Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005). This paper established the current standard-of-care treatment of glioblastoma.
Orringer, D. A. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. 1, 0027 (2017). This paper demonstrates the feasibility of integrating SRH into the clinical workflow for brain tumor diagnosis.
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). This paper shows that deep neural networks can perform as well as pathologists for histologic brain tumor diagnosis.
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017). This paper introduced the transformer neural network architecture.
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This is a summary of: Hollon, T. et al. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat. Med. https://doi.org/10.1038/s41591-023-02252-4 (2023).
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Using AI to improve the molecular classification of brain tumors. Nat Med 29, 793–794 (2023). https://doi.org/10.1038/s41591-023-02298-4
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DOI: https://doi.org/10.1038/s41591-023-02298-4
