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 comparative effectiveness 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 parametrize the biologically necessary bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, along with an illustration of their use in the context of anal canal cancer patients treated with radiotherapy.

翻译：放射治疗癌症的目标是向肿瘤输送规定的辐射剂量，同时最大限度地减少对周围健康组织的剂量。为了评估治疗计划，健康器官的剂量分布通常以剂量体积直方图（DVH）进行总结。正常组织并发症概率（NTCP）建模主要集中于利用从DVH中提取的特征进行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相对有效性。我们基于确定性和随机性干预提出了NTCP的因果估计量，并提出了基于边缘结构模型的估计方法，该模型参数化了剂量、体积与毒性风险之间生物学上必要的双变量单调性。通过模拟研究了这些估计量的性质，并展示了其在接受放疗的肛管癌患者中的应用实例。