调强放射治疗（IMRT）已广泛用于治疗头颈部肿瘤。然而，由于头颈部区域解剖结构复杂，计划优化器快速生成临床可接受的 IMRT 治疗计划具有挑战性。当前的研究开发了一种新型深度学习多尺度 Transformer (MST) 模型，旨在加速头颈部肿瘤的 IMRT 规划，同时对体素水平剂量分布进行更精确的预测。所提出的端到端MST模型采用分流Transformer来捕获多尺度特征并学习全局依赖性，并利用3D可变形卷积瓶颈块来提取形状感知特征并补偿补丁合并层中的空间信息损失。此外，利用数据增强和自知识蒸馏来进一步提高模型的预测性能。 MST 模型在 OpenKBP Challenge 数据集上进行训练和评估。其预测精度与之前的三种剂量预测模型：C3D、TrDosePred 和 TSNet 进行了比较。我们提出的 MST 模型在肿瘤区域的预测剂量分布最接近原始的临床剂量分布。 MST模型在测试数据集上实现了2.23 Gy的剂量分数和1.34 Gy的DVH分数，优于其他三个模型8%-17%。对于临床相关的 DVH 剂量测定指标，平均绝对误差 (MAE) 的预测准确度为 D 99 为 2.04%、D 95 为 1.54%、D 1 为 1.87%、Dmean 为 1.87%、D 0.1 为 1.89% c c 分别优于其他三个模型。定量结果表明，所提出的 MST 模型比以前的头颈肿瘤模型实现了更准确的体素水平剂量预测。 MST 模型具有巨大的潜力，可以应用于其他疾病部位，以进一步提高放射治疗计划的质量和效率。© 2024。澳大利亚物理科学家和医学工程师学院。

Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.© 2024. Australasian College of Physical Scientists and Engineers in Medicine.