The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

中文翻译：

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放射肿瘤学临床决策的马尔可夫模型：系统评价。


患者对治疗反应的内在随机性是放射治疗临床决策的主要考虑因素。马尔可夫模型是捕捉这种随机性并做出有效治疗决策的强大工具。本文概述了放射肿瘤学临床决策分析的马尔可夫模型。根据系统评价和荟萃分析的首选报告项目 (PRISMA) 指南，使用 PubMed 在 MEDLINE 内进行了全面的文献检索。仅考虑 2000 年至 2023 年发表的研究。选定的出版物总结为两类：（i）使用蒙特卡罗模拟比较两种（或更多）固定治疗政策的研究和（ii）通过马尔可夫决策过程（MDP）寻求最佳治疗政策的研究。与本研究范围相关，选择了 61 篇出版物进行详细审查。这些出版物中的大多数（n = 56）侧重于使用蒙特卡罗模拟对两个或多个固定治疗政策进行比较分析。提出了基于癌症部位、效用测量和敏感性分析类型的分类。五份出版物考虑了 MDP，旨在计算最佳治疗政策；每项工作都提供了分析和结果的详细说明。作为基于马尔可夫模型的模拟分析的扩展，MDP 提供了一个灵活的框架，用于在可能的大量治疗策略中识别最佳治疗策略。然而，MDP 在肿瘤决策中的应用尚未得到充分研究，该框架提供复杂的最佳治疗决策的全部能力值得进一步考虑。