Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs-at-risk (OARs), can inform future treatment planning. The radiation exposure to OARs can be summarized by a matrix of dose-volume histograms (DVH) for each patient. Although various methods for high-dimensional mediation analysis have been proposed recently, few studies investigated how matrix-valued data can be treated as mediators. In this paper, we propose a novel Bayesian joint mediation model for high-dimensional matrix-valued mediators. In this joint model, latent features are extracted from the matrix-valued data through an adaptation of probabilistic multilinear principal components analysis (MPCA), retaining the inherent matrix structure. We derive and implement a Gibbs sampling algorithm to jointly estimate all model parameters, and introduce a Varimax rotation method to identify active indicators of mediation among the matrix-valued data. Our simulation study finds that the proposed joint model has higher efficiency in estimating causal decomposition effects compared to an alternative two-step method, and demonstrates that the mediation effects can be identified and visualized in the matrix form. We apply the method to study the effect of prescription dose on treatment interruptions in anal canal cancer patients.

翻译：非计划性治疗中断可能导致放射治疗（RT）护理质量下降。识别RT处方剂量通过分布至不同危及器官（OAR）的剂量对治疗中断结局的影响，可为未来治疗计划制定提供依据。每位患者的放射暴露剂量可由剂量体积直方图（DVH）矩阵进行汇总。尽管近年来已提出多种高维中介分析方法，但鲜有研究探讨如何将矩阵值数据作为中介变量处理。本文针对高维矩阵值中介变量提出一种新型贝叶斯联合中介模型。在该联合模型中，通过概率多线性主成分分析（MPCA）的改进方法从矩阵值数据中提取潜在特征，保留其固有矩阵结构。我们推导并实现了吉布斯采样算法以联合估计所有模型参数，并引入Varimax旋转方法识别矩阵值数据中的活跃中介指标。仿真研究表明，与替代的两步法相比，所提出的联合模型在估计因果分解效应方面具有更高效率，并证明中介效应可被识别并以矩阵形式可视化。我们将该方法应用于研究处方剂量对肛管癌患者治疗中断的影响。