Objective Selection for Cancer Treatment: An Inverse Optimization Approach
Objective Selection for Cancer Treatment: An Inverse Optimization Approach
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DOI：
10.1287/opre.2021.2192
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
T. Ajayi;Taewoo Lee;A. Schaefer
中科院分区：
文献类型：
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作者：
T. Ajayi;Taewoo Lee;A. Schaefer
放射治疗计划的质量和计划过程的效率在很大程度上受计划目标选择的影响。虽然简单的目标可以实现有效的治疗计划，但所产生的治疗质量可能在临床上不可接受;复杂的目标可以产生高质量的治疗，但计划过程在计算上变得令人望而却步。在“Objective Selection for Cancer Treatment：An Inverse Optimization Approach”中，通过整合逆向优化和特征选择技术，Ajayi、Lee和Schaefer提出了一种新的目标选择方法，该方法使用历史放射治疗治疗数据来推断一组易于处理且简约但临床有效的规划目标。虽然目标选择问题是一个大规模的双层混合整数规划，作者提出了各种解决方案的启发，特征选择贪婪算法和患者特定的解剖特征。
The quality of radiation therapy treatment plans and the efficiency of the planning process are heavily affected by the choice of planning objectives. Although simple objectives enable efficient treatment planning, the resulting treatment quality might not be clinically acceptable; complex objectives can generate high-quality treatment, yet the planning process becomes computationally prohibitive. In “Objective Selection for Cancer Treatment: An Inverse Optimization Approach,” by integrating inverse optimization and feature selection techniques, Ajayi, Lee, and Schaefer propose a novel objective selection method that uses historical radiation therapy treatment data to infer a set of planning objectives that are tractable and parsimonious yet clinically effective. Although the objective selection problem is a large-scale bilevel mixed-integer program, the authors propose various solution approaches inspired by feature selection greedy algorithms and patient-specific anatomical characteristics.