质子治疗中的患者选择的数学建模。
Mathematical Modelling for Patient Selection in Proton Therapy.
机构信息
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University, Manchester Academic Health Science Centre, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University, Manchester Academic Health Science Centre, Manchester, UK.
出版信息
Clin Oncol (R Coll Radiol). 2018 May;30(5):299-306. doi: 10.1016/j.clon.2018.01.007. Epub 2018 Feb 14.
Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.
质子束疗法（PBT）在癌症治疗中仍相对较新，临床证据基础相对较少。数学建模在选择 PBT 患者和预测服务需求方面提供了帮助。离散事件模拟、正常组织并发症概率、质量调整生命年和马尔可夫链模型都是目前使用的数学和统计建模技术，但没有一种技术占主导地位。随着来自 PBT 的新证据和结果数据的出现，将出现不那么依赖于放射治疗计划和交付特定技术的综合模型。
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