比较放射治疗计划的优化模型

中文摘要 AI 辅助

放射治疗计划问题（RTSP）涉及为接受放射治疗的患者确定最佳计划，鉴于放射治疗在癌症治疗中的核心作用，该任务对临床结果有重大影响。目前，每日批量方法（即在每天结束时安排所有新到患者）通过整数线性规划建模，是RTSP最有效的方法之一，但这种公式在时间和内存方面需要大量计算资源。本文通过开发两种新颖的贪婪启发式算法（RTSP首次适配和RTSP最佳适配）来解决这些限制，并将其用作模拟退火（SA）方法的建设性启发式算法以优化计划。在公开可用数据集上，将所提出的方法（单独的启发式算法及其与SA的组合）与使用两种不同的最先进精确求解器求解的整数线性规划公式进行评估。评估指标包括六个计划目标，涵盖患者等待时间、偏好满意度以及直线加速器分配的变化（在四种不同权重配置中汇总）、求解时间和内存消耗。结果表明，新颖的启发式算法实现了接近精确方法的解决方案，同时显著减少了运行时间和内存使用；此外，与SA结合时，它们在保持低运行时间和内存使用的同时进一步提高了解决方案质量。

英文摘要

The Radiotherapy Scheduling Problem (RTSP) involves determining an optimal schedule for patients undergoing radiation treatments, a task that has a massive impact on clinical outcomes given the central role of radiotherapy in cancer care. The daily batch approach--which consists of scheduling all the newly arrived patients together at the end of each day--modelled with Integer Linear Programming, is currently one of the most effective methods for the RTSP. However, this kind of formulation requires substantial computational resources in terms of time and memory. Here, we address these limitations by developing two novel greedy heuristics (named RTSP First Fit and RTSP Best Fit) and use them as constructive heuristics for a Simulated Annealing (SA) approach to optimize the scheduling. The proposed methods--the heuristics alone and their combination with SA--are evaluated on a publicly available dataset against an integer linear program formulation solved with two different state-of-the-art exact solvers. Evaluation metrics include six scheduling objectives capturing patient waiting times, preference satisfaction, and changes in linear accelerator assignment (aggregated in four different weight configurations), solving time, and memory consumption. The results show that the novel heuristics achieve solutions close to those of exact methods, while dramatically reducing runtime and memory usage; furthermore, when combined with SA, they further improve the solution quality while maintaining low runtime and memory usage.

发表机构

• University of Trento（特伦托大学）
• University of Pisa（比萨大学）

机构由 AI 辅助整理，请以论文原文为准。