Louis, D. N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 23, 1231–1251 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Damodaran, S., Berger, M. F. & Roychowdhury, S. Clinical tumor sequencing: opportunities and challenges for precision cancer medicine. Am. Soc. Clin. Oncol. Educ. Book 2015, e175–e182 (2015).

Brat, D. J. et al. Molecular biomarker testing for the diagnosis of diffuse gliomas. Arch. Pathol. Lab. Med 146, 547–574 (2022).

Article  PubMed  PubMed Central  Google Scholar 

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).

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  PubMed  PubMed Central  Google Scholar 

Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. in Proceedings of the 37th International Conference on Machine Learning (eds Iii, H. D. & Singh, A.) 1597–1607 (PMLR, 2020).

Eckel-Passow, J. E. et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N. Engl. J. Med. 372, 2499–2508 (2015).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cancer Genome Atlas Research Network et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).

Article  Google Scholar 

Pennington, J., Socher, R. & Manning, C. GloVe: global vectors for word representation. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics, 2014).

Vaswani, A. et al. Attention is all you need. in Advances in Neural Information Processing Systems (eds Guyon, I. et al.) 30 (Curran Associates, 2017).

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) 4171–4186 (Association for Computational Linguistics, 2019).

DeWitt, J. C. et al. Cost-effectiveness of IDH testing in diffuse gliomas according to the 2016 WHO classification of tumors of the central nervous system recommendations. Neuro Oncol. 19, 1640–1650 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Louis, D. N. et al. cIMPACT-NOW (the consortium to inform molecular and practical approaches to CNS tumor taxonomy): a new initiative in advancing nervous system tumor classification. Brain Pathol. 27, 851–852 (2017).

Article  PubMed  Google Scholar 

Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Drexler, R. et al. DNA methylation subclasses predict the benefit from gross total tumor resection in IDH-wildtype glioblastoma patients. Neuro Oncol. 25, 315–325 (2022).

Hervey-Jumper, S. L. et al. Interactive effects of molecular, therapeutic, and patient factors on outcome of diffuse low-grade glioma. J. Clin. Oncol. https://doi.org/10.1200/JCO.21.02929 (2023).

Vanderbeek, A. M. et al. The clinical trials landscape for glioblastoma: is it adequate to develop new treatments? Neuro Oncol. 20, 1034–1043 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Frome, A. et al. DeViSE: a deep visual–semantic embedding model. in Advances in Neural Information Processing Systems (eds Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z. & Weinberger, K. Q.) 26 (Curran Associates, 2013).

Ramesh, A. et al. Zero-shot text-to-image generation. in Proceedings of the 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 8821–8831 (PMLR, 2021).

Saharia, C. et al. Photorealistic text-to-image diffusion models with deep language understanding. Preprint at arXiv https://doi.org/10.48550/arXiv.2205.11487(2022).

Radford, A. et al. Learning transferable visual models from natural language supervision. in Proceedings of the 38th International Conference on Machine Learning Vol. 139 (eds. Meila, M. & Zhang, T.) 8748–8763 (PMLR, 2021).

Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Freudiger, C. W. et al. Stimulated Raman scattering microscopy with a robust fibre laser source. Nat. Photonics 8, 153–159 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hollon, T. C. et al. Rapid intraoperative diagnosis of pediatric brain tumors using stimulated Raman histology. Cancer Res. 78, 278–289 (2018).

Article  CAS  PubMed  Google Scholar 

Hollon, T. C. et al. Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks. Neuro Oncol. 23, 144–155 (2021).

He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90 (2016).

Jiang, C. et al. Rapid automated analysis of skull base tumor specimens using intraoperative optical imaging and artificial intelligence. Neurosurgery 90, 758–767 (2022).

Article  PubMed  Google Scholar 

Zhao, Z. et al. Chinese Glioma Genome Atlas (CGGA): a comprehensive resource with functional genomic data from Chinese glioma patients. Genomics Proteomics Bioinformatics 19, 1–12 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang, J. et al. The International Cancer Genome Consortium Data Portal. Nat. Biotechnol. 37, 367–369 (2019).

Article  CAS  PubMed  Google Scholar 

Gusev, Y. et al. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci. Data 5, 180158 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jonsson, P. et al. Genomic correlates of disease progression and treatment response in prospectively characterized gliomas. Clin. Cancer Res. 25, 5537–5547 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Du, J. et al. Gene2vec: distributed representation of genes based on co-expression. BMC Genomics 20, 82 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lanchantin, J., Wang, T., Ordonez, V. & Qi, Y. General multi-label image classification with transformers. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.01621 (2021).

Dosovitskiy, A. et al. An image is worth 16×16 words: transformers for image recognition at scale. Preprint at arXiv https://doi.org/10.48550/arXiv.2010.11929 (2020).

Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).

Article  CAS  PubMed  Google Scholar