  Radiation therapy is the primary method used to treat cancer in the clinic.
Its goal is to deliver a precise dose to the planning target volume (PTV) while
protecting the surrounding organs at risk (OARs). However, the traditional
workflow used by dosimetrists to plan the treatment is time-consuming and
subjective, requiring iterative adjustments based on their experience. Deep
learning methods can be used to predict dose distribution maps to address these
limitations. The study proposes a cascade model for organs at risk segmentation
and dose distribution prediction. An encoder-decoder network has been developed
for the segmentation task, in which the encoder consists of transformer blocks,
and the decoder uses multi-scale convolutional blocks. Another cascade
encoder-decoder network has been proposed for dose distribution prediction
using a pyramid architecture. The proposed model has been evaluated using an
in-house head and neck cancer dataset of 96 patients and OpenKBP, a public head
and neck cancer dataset of 340 patients. The segmentation subnet achieved 0.79
and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the
existing baselines. The dose distribution prediction subnet outperformed the
winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVH
scores, respectively. The predicted dose maps showed good coincidence with
ground truth, with a superiority after linking with the auxiliary segmentation
task. The proposed model outperformed state-of-the-art methods, especially in
regions with low prescribed doses.
