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：放射治疗癌症的目标是将处方辐射剂量精准输送至肿瘤，同时最大限度减少对周围健康组织的照射。为评估治疗方案，健康器官的剂量分布通常通过剂量-体积直方图（DVHs）进行汇总。正常组织并发症概率（NTCP）建模主要围绕从DVHs中提取特征开展患者层面的风险预测，但很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。我们基于确定性和随机干预提出了NTCP的因果估计量，并基于边缘结构模型提出了相应估计方法，该方法对剂量、体积与毒性风险之间施加了双变量单调性约束。通过模拟研究评估了这些估计量的性质，并在肛管癌患者的放射治疗案例中展示了其应用。