Markov models for clinical decision-making in radiation oncology: A systematic review

IF 2.2 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Lucas B McCullum, Aysenur Karagoz, Cem Dede, Raul Garcia, Fatemeh Nosrat, Dr Mehdi Hemmati, Dr Seyedmohammadhossein Hosseinian, Andrew J Schaefer, Dr Clifton D Fuller, the Rice/MD Anderson Center for Operations Research in Cancer (CORC), MD Anderson Head and Neck Cancer Symptom Working Group
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Abstract

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.

Abstract Image

用于放射肿瘤学临床决策的马尔可夫模型:系统综述。
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有力工具。本文概述了用于放射肿瘤学临床决策分析的马尔可夫模型。按照系统综述和荟萃分析首选报告项目(PRISMA)指南,我们使用 PubMed 在 MEDLINE 上进行了全面的文献检索。仅考虑了 2000 年至 2023 年间发表的研究。所选出版物归纳为两类:(i) 使用蒙特卡罗模拟比较两种(或多种)固定治疗策略的研究;(ii) 通过马尔可夫决策过程(MDP)寻求最佳治疗策略的研究。与本研究范围相关的 61 篇出版物被选中进行详细审查。其中大部分(n = 56)侧重于使用蒙特卡罗模拟对两种或多种固定治疗策略进行比较分析。根据癌症部位、效用指标和敏感性分析类型进行了分类。五篇论文考虑了以计算最佳治疗策略为目的的 MDP;每篇论文都提供了分析和结果的详细说明。作为基于马尔可夫模型模拟分析的扩展,MDP 提供了一个灵活的框架,可从可能存在的大量治疗政策中找出最佳治疗政策。然而,MDP 在肿瘤决策中的应用还没有得到充分的研究,这一框架在提供复杂的最佳治疗决策方面的全部能力值得进一步考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
6.20%
发文量
133
审稿时长
6-12 weeks
期刊介绍: Journal of Medical Imaging and Radiation Oncology (formerly Australasian Radiology) is the official journal of The Royal Australian and New Zealand College of Radiologists, publishing articles of scientific excellence in radiology and radiation oncology. Manuscripts are judged on the basis of their contribution of original data and ideas or interpretation. All articles are peer reviewed.
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