  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.
