从模式到患者：癌症诊断、预后和治疗的临床机器学习进展。
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.
机构信息
Department of Computer Science, Stanford University, Stanford, CA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
出版信息
Cell. 2023 Apr 13;186(8):1772-1791. doi: 10.1016/j.cell.2023.01.035. Epub 2023 Mar 10.
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.
机器学习（ML）在临床肿瘤学中越来越多地用于诊断癌症、预测患者预后并为治疗计划提供信息。在这里，我们回顾了 ML 在整个临床肿瘤学工作流程中的最新应用。我们回顾了这些技术如何应用于医学成像以及从液体和实体肿瘤活检中获得的分子数据，以用于癌症诊断、预后和治疗设计。我们讨论了在开发用于解决成像和分子数据带来的独特挑战的 ML 时的关键考虑因素。最后，我们检查了监管机构批准用于癌症相关患者使用的 ML 模型，并讨论了提高 ML 的临床实用性的方法。
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