生物
工作流程
癌症
医学物理学
癌症治疗
精确肿瘤学
临床肿瘤学
内科学
重症监护医学
生物信息学
肿瘤科
计算机科学
医学
数据库
作者
Kyle Swanson,Eric Q. Wu,Angela Zhang,Ash A. Alizadeh,James Zou
出处
期刊:Cell
[Elsevier]
日期:2023-04-01
卷期号:186 (8): 1772-1791
被引量:114
标识
DOI:10.1016/j.cell.2023.01.035
摘要
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
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