A new paradigm for cytology-based artificial intelligence-assisted prediction for cancers of unknown primary origins
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REFERENCES
[1] Tian, F., Liu, D., Wei, N., et al. (2024). Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat. Med. 30: 1309−1319. DOI: 10.1038/s41591-024-02915-w. [2] Rassy, E. and Pavlidis, N. (2020). Progress in refining the clinical management of cancer of unknown primary in the molecular era. Nat. Rev. Clin. Oncol. 17: 541−554. DOI: 10.1038/s41571-020-0359-1. [3] Nguyen, L., Van Hoeck, A., and Cuppen, E. (2022). Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features. Nat. Commun. 13: 4013. DOI: 10.1038/s41467-022-31666-w. [4] Darmofal, M., Suman, S., Atwal, G., et al. (2024). Deep-learning model for tumor-type prediction using targeted clinical genomic sequencing data. Cancer Discov. 14: 1064−1081. DOI: 10.1158/2159-8290.Cd-23-0996. [5] Lu, M.Y., Chen, T.Y., Williamson, D.F.K., et al. (2021). Ai-based pathology predicts origins for cancers of unknown primary. Nature 594: 106−110. DOI: 10.1038/s41586-021-03512-4. -
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Li H., Wang S., Zhang Y., et al., (2024). A new paradigm for cytology-based artificial intelligence-assisted prediction for cancers of unknown primary origins. The Innovation Life 2(3): 100086. https://doi.org/10.59717/j.xinn-life.2024.100086
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Li H., Wang S., Zhang Y., et al., (2024). A new paradigm for cytology-based artificial intelligence-assisted prediction for cancers of unknown primary origins. The Innovation Life 2(3): 100086. https://doi.org/10.59717/j.xinn-life.2024.100086
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Figure 1.
TORCH workflow, advantages, limitations and prospects
