Evidence is accumulating in favour of using stereotactic ablative body radiotherapy (SABR) to treat multiple cancer lesions in the lung. Multi-lesion lung SABR plans are complex and require significant resources to create. In this work, we propose a novel two-stage latent transformer framework (LDFormer) for dose prediction of lung SABR plans with varying numbers of lesions. In the first stage, patient anatomical information and the dose distribution are encoded into a latent space. In the second stage, a transformer learns to predict the dose latent from the anatomical latents. Causal attention is modified to adapt to different numbers of lesions. LDFormer outperforms a state-of-the-art generative adversarial network on dose conformality in and around lesions, and the performance gap widens when considering overlapping lesions. LDFormer generates predictions of 3-D dose distributions in under 30s on consumer hardware, and has the potential to assist physicians with clinical decision making, reduce resource costs, and accelerate treatment planning.

本研究提出了一种新颖的两阶段潜变形自适应模型（LDFormer），用于具有不同数量病灶的肺癌SABR计划的剂量预测。LDFormer在病灶及其周围的剂量一致性方面优于现有技术，并且在考虑重叠病灶时性能差距扩大。LDFormer可以在30秒内在普通硬件上生成三维剂量分布的预测，有潜力辅助医生进行临床决策，降低资源成本和加速治疗规划。

基于潜变量空间的复杂放疗计划中的变压器剂量预测