剂量预测是肝癌自动放射治疗计划的关键步骤。人们提出了几种基于深度学习的剂量预测方法来提高放疗计划的设计效率和质量。然而，这些方法通常将CT图像、危及器官（OAR）轮廓和计划靶体积（PTV）作为多通道输入，因此很难从每个输入中提取足够的特征信息，从而导致剂量分布不理想。在本文中，我们提出了一种基于分层特征融合和交互式关注的新型肝癌剂量预测网络。首先构建特征提取模块以从不同输入中提取多尺度特征，然后构建分层特征融合模块以分层融合这些多尺度特征。设计了基于注意力机制的解码器，将融合特征逐渐重建为剂量分布。此外，我们设计了一个自动编码器网络来在训练阶段产生感知损失，用于提高剂量预测的准确性。该方法在私人临床数据集上进行了测试，获得的 HI 和 CI 分别为 0.31 和 0.87。实验结果优于几种现有方法，表明该方法产生的剂量分布接近临床批准的剂量分布。这些代码可在 https://github.com/hired-ld/FA-Net 上获取。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Dose prediction is a crucial step in automated radiotherapy planning for liver cancer. Several deep learning-based approaches for dose prediction have been proposed to enhance the design efficiency and quality of radiotherapy plan. However, these approaches usually take CT images and contours of organs at risk (OARs) and planning target volume (PTV) as a multi-channel input and is thus difficult to extract sufficient feature information from each input, which results in unsatisfactory dose distribution. In this paper, we propose a novel dose prediction network for liver cancer based on hierarchical feature fusion and interactive attention. A feature extraction module is first constructed to extract multi-scale features from different inputs, and a hierarchical feature fusion module is then built to fuse these multi-scale features hierarchically. A decoder based on attention mechanism is designed to gradually reconstruct the fused features into dose distribution. Additionally, we design an autoencoder network to generate a perceptual loss during training stage, which is used to improve the accuracy of dose prediction. The proposed method is tested on private clinical dataset and obtains HI and CI of 0.31 and 0.87, respectively. The experimental results are better than those by several existing methods, indicating that the dose distribution generated by the proposed method is close to that approved in clinics. The codes are available at https://github.com/hired-ld/FA-Net.Copyright © 2024 Elsevier B.V. All rights reserved.