From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

被引：285

作者：

Swanson, Kyle ^{[1
]}

Wu, Eric ^{[2
]}

Zhang, Angela ^{[3
]}

Alizadeh, Ash A. ^{[4
]}

Zou, James ^{[1
,2
,5
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Genet, Stanford, CA USA

[4] Stanford Univ, Dept Med, Stanford, CA USA

[5] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA

来源：

CELL | 2023年 / 186卷 / 08期

基金：

美国国家卫生研究院;

关键词：

ARTIFICIAL-INTELLIGENCE; GENE-EXPRESSION; LUNG-CANCER; SKIN-CANCER; DEEP; SYSTEM; CLASSIFICATION; VALIDATION; BIOPSIES; DENSITY;

D O I：

10.1016/j.cell.2023.01.035

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient out-comes, 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.

引用

页码：1772 / 1791

页数：20

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