A Quantitative Paradigm for Decision-Making in Precision Oncology

被引：7

作者：

Engelhardt, Dalit ^{[1
,2
,3
,4
]}

Michor, Franziska ^{[1
,2
,3
,4
,5
,6
]}

机构：

[1] Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02115 USA

[2] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[3] Harvard Univ, Dept Stem Cell & Regenerat Biol, Cambridge, MA 02138 USA

[4] Dana Farber Canc Inst, Ctr Canc Evolut, Boston, MA 02115 USA

[5] Broad Inst Harvard & MIT, Cambridge, MA USA

[6] Ludwig Ctr Harvard, Boston, MA USA

来源：

TRENDS IN CANCER | 2021年 / 7卷 / 04期

关键词：

2-STAGE RANDOMIZATION DESIGNS; HIGH-RISK NEUROBLASTOMA; OLDER PATIENTS; SURVIVAL DISTRIBUTIONS; TREATMENT STRATEGIES; TREATMENT POLICIES; THERAPY; CANCER; TRIAL; CHEMOTHERAPY;

D O I：

10.1016/j.trecan.2021.01.006

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.

引用

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页码：293 / 300

页数：8

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