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.

翻译：越来越多的证据支持使用立体定向消融体部放疗（SABR）来治疗肺部的多个癌性病灶。多病灶肺部SABR计划非常复杂，需要大量资源来制定。在这项工作中，我们提出了一种新颖的两阶段潜在Transformer框架（LDFormer），用于预测具有不同病灶数量的肺部SABR计划的剂量分布。在第一阶段，患者的解剖学信息和剂量分布被编码到一个潜在空间中。在第二阶段，一个Transformer学习从解剖学潜在表示中预测剂量潜在表示。因果注意力机制经过修改以适应不同数量的病灶。在病灶内部及周围的剂量适形性方面，LDFormer优于最先进的生成对抗网络，并且在考虑重叠病灶时，性能差距进一步扩大。LDFormer在消费级硬件上能在30秒内生成三维剂量分布的预测，具有辅助医生进行临床决策、降低资源成本和加速治疗计划的潜力。