Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBCT) acquisitions to monitor changes, but have yet to be used to improve tumor control while managing side-effects. The aim of this study is to demonstrate the clinical value of pre-treatment CBCT acquired daily during radiation therapy treatment for head and neck cancers for the downstream task of predicting severe toxicity occurrence: reactive feeding tube (NG), hospitalization and radionecrosis. For this, we propose a deformable 3D classification pipeline that includes a component analyzing the Jacobian matrix of the deformation between planning CT and longitudinal CBCT, as well as clinical data. The model is based on a multi-branch 3D residual convolutional neural network, while the CT to CBCT registration is based on a pair of VoxelMorph architectures. Accuracies of 85.8% and 75.3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment. For NG tube risk, performance improves with increasing the timing of the CBCT fraction, reaching 83.1% after the $5_{th}$ week of treatment.

翻译：自适应放疗是癌症治疗中一个不断发展的研究领域，其目标是保护健康组织。多家机构的治疗标准包括纵向锥形束计算机断层扫描（CBCT）采集以监测变化，但尚未被用于在管理副作用的同时改善肿瘤控制。本研究的目的是证明放疗期间每日采集的治疗前CBCT在预测严重毒性发生（如反应性饲管（NG）、住院和放射性坏死）的下游任务中的临床价值。为此，我们提出一个可变形3D分类流程，其中包含一个分析计划CT与纵向CBCT之间形变的雅可比矩阵的组件，以及临床数据。该模型基于多分支3D残差卷积神经网络，而CT到CBCT的配准基于一对VoxelMorph架构。放射性坏死和住院的准确率分别达到85.8%和75.3%，并且在治疗第一周后即表现出相似的性能。对于NG管风险，性能随着CBCT分次时间点的增加而提升，在治疗第5周后达到83.1%。