  A novel dose calculation approach was designed based on the application of
LSTM network that processes the 3D patient/phantom geometry as a sequence of 2D
computed tomography input slices yielding a corresponding sequence of 2D slices
that forms the respective 3D dose distribution. LSTM networks can propagate
information effectively in one direction, resulting in a model that can
properly imitate the mechanisms of proton interaction in matter. The study is
centered on predicting dose on a single pencil beam level, avoiding the
averaging effects in treatment plans comprised of thousands pencil beams.
Moreover, such approach allows straightforward integration into today's
treatment planning systems' inverse planning optimization process. The ground
truth training data was prepared with Monte Carlo simulations for both phantom
and patient studies by simulating different pencil beams impinging from random
gantry angles through the patient geometry. For model training, 10'000 Monte
Carlo simulations were prepared for the phantom study, and 4'000 simulations
were prepared for the patient study. The trained LSTM model was able to achieve
a 99.29 % gamma-index pass rate ([0.5 %, 1 mm]) accuracy on the set-aside test
set for the phantom study, and a 99.33 % gamma-index pass rate ([0.5 %, 2 mm])
for the set-aside test set for the patient study. These results were achieved
for each pencil beam in 6-23 ms. The average Monte Carlo simulation run-time
using Topas was 1160 s. The generalization of the model was verified by testing
for 5 previously unseen lung cancer patients. LSTM networks are well suited for
proton therapy dose calculation tasks. However, further work needs to be
performed to generalize the proposed approach to clinical applications,
primarily to be implemented for various energies, patient sites, and CT
resolutions/scanners.
