利用基于细胞学的深度学习预测肿瘤来源：炒作还是希望？

IF 81.1 1区医学 Q1 ONCOLOGY

Nature Reviews Clinical Oncology Pub Date : 2024-05-21 DOI:10.1038/s41571-024-00906-x

Elie Rassy, Nicholas Pavlidis

引用次数: 0

摘要

大多数原发灶不明的癌症患者在接受经验性化疗后都会出现不良后果。向使用基于精准医学的治疗策略转变涉及两种选择：组织诊断或特定部位方法。在此，我们将探讨如何在这些方法中利用基于细胞学的深度学习工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

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来源期刊

Nature Reviews Clinical Oncology 医学-肿瘤学

CiteScore

99.40

自引率

0.40%

发文量

114

审稿时长

6-12 weeks

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.