自适应放疗（ART）是现代癌症治疗中的一项先进技术，它将病人解剖结构中的渐进性变化融入分段治疗中的计划/剂量调整。然而，临床应用需要对低质量的随机图像精确分割癌症肿瘤，这对手动划分和深度学习模型都带来了挑战。在本文中，我们提出了一种新颖的序列转导深度神经网络，其具有注意力机制，可以基于患者每周的锥束计算机断层扫描（CBCT）学习癌症肿瘤的收缩。我们设计了一种自我监督的领域适应方法（SDA），以学习和适应预处理高质量计算机断层扫描（CT）的丰富纹理和空间特征，以解决图像质量差和缺乏标签的问题。我们还为序列分割提供了不确定性估计，这不仅有助于治疗计划的风险管理，还能改善模型的校准和可靠性。我们基于一个包含十六名患者和96个纵向CBCT的临床非小细胞肺癌（NSCLC）数据集进行的实验结果表明，我们的模型能正确学习肿瘤的周变形，平均骰子分数为0.92，且能够预测未来患者治疗的多个步骤（多达5周），平均骰子分数降低了0.05。通过将肿瘤收缩预测融入每周重新规划策略中，我们提出的方法在保持高肿瘤控制概率的同时，成功减少了放射性肺炎的风险高达35%。版权所有 © 2023 Elsevier B.V. 发表。

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that incorporates progressive changes in patient anatomy into active plan/dose adaption during the fractionated treatment. However, the clinical application relies on the accurate segmentation of cancer tumors on low-quality on-board images, which has posed challenges for both manual delineation and deep learning-based models. In this paper, we propose a novel sequence transduction deep neural network with an attention mechanism to learn the shrinkage of the cancer tumor based on patients' weekly cone-beam computed tomography (CBCT). We design a self-supervised domain adaption (SDA) method to learn and adapt the rich textural and spatial features from pre-treatment high-quality computed tomography (CT) to CBCT modality in order to address the poor image quality and lack of labels. We also provide uncertainty estimation for sequential segmentation, which aids not only in the risk management of treatment planning but also in the calibration and reliability of the model. Our experimental results based on a clinical non-small cell lung cancer (NSCLC) dataset with sixteen patients and ninety-six longitudinal CBCTs show that our model correctly learns weekly deformation of the tumor over time with an average dice score of 0.92 on the immediate next step, and is able to predict multiple steps (up to 5 weeks) for future patient treatments with an average dice score reduction of 0.05. By incorporating the tumor shrinkage predictions into a weekly re-planning strategy, our proposed method demonstrates a significant decrease in the risk of radiation-induced pneumonitis up to 35% while maintaining the high tumor control probability.Copyright © 2023. Published by Elsevier B.V.