The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modelling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

翻译：癌症放射治疗的目标是在向肿瘤输送处方辐射剂量的同时，最小化对周围健康组织的照射剂量。为评估治疗方案，健康器官的剂量分布通常以剂量-体积直方图（DVH）进行汇总。正常组织并发症概率（NTCP）建模的核心在于利用从DVH中提取的特征进行患者层面的风险预测，但少有研究考虑采用因果框架来评估替代治疗方案的安全性。我们基于确定性及随机性干预提出了NTCP的因果估计量，并建立了基于边际结构模型的估计方法，该方法强制要求剂量、体积与毒性风险之间存在双变量单调性。通过模拟研究验证了这些估计量的统计特性，并在肛管癌患者放射治疗的背景下展示了其应用价值。