{"title":"利用基于细胞学的深度学习预测肿瘤来源：炒作还是希望？","authors":"Elie Rassy, Nicholas Pavlidis","doi":"10.1038/s41571-024-00906-x","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19079,"journal":{"name":"Nature Reviews Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":81.1000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting tumour origin with cytology-based deep learning: hype or hope?\",\"authors\":\"Elie Rassy, Nicholas Pavlidis\",\"doi\":\"10.1038/s41571-024-00906-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":19079,\"journal\":{\"name\":\"Nature Reviews Clinical Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":81.1000,\"publicationDate\":\"2024-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Clinical Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.nature.com/articles/s41571-024-00906-x\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Clinical Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41571-024-00906-x","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Predicting tumour origin with cytology-based deep learning: hype or hope?
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.
期刊介绍：
Nature Reviews publishes clinical content authored by internationally renowned clinical academics and researchers, catering to readers in the medical sciences at postgraduate levels and beyond. Although targeted at practicing doctors, researchers, and academics within specific specialties, the aim is to ensure accessibility for readers across various medical disciplines. The journal features in-depth Reviews offering authoritative and current information, contextualizing topics within the history and development of a field. Perspectives, News & Views articles, and the Research Highlights section provide topical discussions, opinions, and filtered primary research from diverse medical journals.
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