The majority of patients with cancers of unknown primary have unfavourable outcomes when they receive empirical chemotherapy. The shift towards using precision medicine-based treatment strategies involves two options: tissue-agnostic or site-specific approaches. Here, we reflect on how cytology-based deep learning tools can be leveraged in these approaches.
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E.R. reports institutional grants from Gilead; travel, accommodations and expenses fees from Eli Lilly, Gilead, Mundipharma, Novartis, Pfizer and Roche; and honoraria for lectures and presentations from Eli Lilly, Novartis and Seagen, none of which are related to the treatment of CUP. N.P. declares no competing interests.
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Rassy, E., Pavlidis, N. Predicting tumour origin with cytology-based deep learning: hype or hope?. Nat Rev Clin Oncol (2024). https://doi.org/10.1038/s41571-024-00906-x
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DOI: https://doi.org/10.1038/s41571-024-00906-x
