A Deep Learning Model for Cancer Type Prediction Sets a New Standard.

IF 29.7 1区医学 Q1 ONCOLOGY

Cancer discovery Pub Date : 2024-06-03 DOI:10.1158/2159-8290.CD-24-0280

Salil Garg

引用次数: 0

Abstract

Summary: Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).

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用于癌症类型预测的深度学习模型树立了新标准。

摘要：使用机器学习方法对肿瘤类型进行分类并非总是轻而易举的事，尤其是对于原发灶不明的癌症等具有挑战性的病例。在本期《癌症发现》（Cancer Discovery）杂志上，Darmofal及其同事介绍了一种利用临床测序面板信息诊断肿瘤类型的新工具，并表明该模型特别稳健。请参阅 Darmofal 等人的相关文章，第 1064 页（1）。

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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来源期刊

Cancer discovery ONCOLOGY-

CiteScore

22.90

自引率

1.40%

发文量

838

审稿时长

6-12 weeks

期刊介绍： Cancer Discovery publishes high-impact, peer-reviewed articles detailing significant advances in both research and clinical trials. Serving as a premier cancer information resource, the journal also features Review Articles, Perspectives, Commentaries, News stories, and Research Watch summaries to keep readers abreast of the latest findings in the field. Covering a wide range of topics, from laboratory research to clinical trials and epidemiologic studies, Cancer Discovery spans the entire spectrum of cancer research and medicine.

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