Radiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain. We present PREVIS: a visual analytics tool for (i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and (ii) the assessment of treatment strategies, with respect to the anticipated changes. Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient’s organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy. We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers.

放射治疗（RT）需要在治疗前进行周密的计划，其中在治疗前对患者进行计算机断层扫描（CT）扫描时，通过器官描绘来优化RT计划。常规分次治疗通常持续数周。在治疗患者时，会发生随机变化（例如，前列腺癌患者的直肠和膀胱充盈）和系统性变化（例如，体重减轻）。因此，传递的剂量分布可能会偏离计划。现代技术，特别是图像指导，可以使这些偏差减至最小，但仍会给患者带来风险。我们介绍PREVIS：一种视觉分析工具，用于（i）在即将到来的治疗期间探索和预测患者解剖结构的变化，以及（ii）评估与预期变化有关的治疗策略。PREVIS采用回顾性影像学队列中治疗过程中变化的记录以及完整的数据，使用生成模型来推断不完整数据的新来患者的预期解剖结构变化。回顾性队列划分的抽象表示提供了对基础自动聚类的洞察力，显示了过去患者的主要变异模式。交互式相似性表示支持对新入院患者和既往患者之间的匹配进行知情选择。基于主成分分析（PCA）的生成模型基于对过去患者的观察结果，描述了在即将到来的治疗几周内传入患者器官的预测空间概率分布。生成模型与治疗计划评估相互关联，从而支持最佳治疗策略的选择。我们提出了一个使用场景，演示了PREVIS在临床研究环境中，我们与八名临床研究人员一起评估了我们的视觉分析工具。