Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA.
Department of Radiation Oncology, Ludwig-Maximilians University, Munich, Germany.
Radiother Oncol. 2019 May;134:96-100. doi: 10.1016/j.radonc.2019.01.010. Epub 2019 Feb 6.
A typical fractionated radiotherapy (RT) course is a long and arduous process, demanding significant financial, physical, and mental commitments from patients. Each additional session of RT significantly increases the physical and psychological burden on patients and leads to higher radiation exposure in organs-at-risk (OAR), while, in some cases, the therapeutic benefits might not be high enough to justify the risks. Today, through technological advancements in molecular biology, imaging, and genetics more information is gathered about individual patient response before, during, and after the treatment. we highlight some of the ways that mathematical tools can help assess treatment efficacy on the fly, adapt the treatment plan based on individual biological response, and optimally stop the treatment, if necessary. We term this "Optimal Stopping in RT (OSRT)", after a similar concept in the fields of dynamic programming and Markov decision processes. In short, OSRT can dynamically determine "whether, when and how" to stop the treatment to improve therapeutic ratios.
典型的分割放疗（RT）过程是一个漫长而艰巨的过程，需要患者在经济、身体和精神上做出重大承诺。每次额外的 RT 治疗都会显著增加患者的身体和心理负担，并导致危及器官（OAR）的辐射暴露增加，而在某些情况下，治疗效果可能不足以证明风险是合理的。如今，通过分子生物学、成像和遗传学技术的进步，在治疗前、治疗中和治疗后可以收集更多关于个体患者反应的信息。我们强调了一些数学工具可以帮助评估治疗效果的方法，根据个体生物反应调整治疗计划，并在必要时优化停止治疗。我们将其称为“RT 中的最佳停止（OSRT）”，这是动态规划和马尔可夫决策过程等领域类似概念的延伸。简而言之，OSRT 可以动态地确定“是否、何时以及如何”停止治疗以提高治疗比率。
