  Purpose: To develop a knowledge-based voxel-wise dose prediction system using
a convolution neural network for high-dose-rate brachytherapy cervical cancer
treatments with a tandem-and-ovoid (T&O) applicator. Methods: A 3D U-NET was
utilized to output dose predictions using organ-at-risk (OAR), high-risk
clinical target volume (HRCTV), and possible source locations. A sample of
previous T&O treatments comprising 397 cases (273 training:62 validation:62
test), HRCTV and OARs (bladder/rectum/sigmoid) was used. Structures and dose
were interpolated to 1x1x2.5mm3 dose planes with two input channels (source
positions, voxel identification) and one output channel for dose. We evaluated
dose difference (\Delta D)(xyz)=D_(actual)(x,y,z)-D_(predicted)(x,y,z) and dice
similarity coefficients in all cohorts across the clinically-relevant dose
range (20-130% of prescription), mean and standard deviation. We also examined
discrete DVH metrics used for T&O plan quality assessment: HRCTV D_90%(dose to
hottest 90% volume) and OAR D_2cc, with \Delta
D_x=D_(x,actual)-D_(x,predicted). Pearson correlation coefficient, standard
deviation, and mean quantified model performance on the clinical metrics.
Results: Voxel-wise dose difference accuracy for 20-130% dose range for
training(test) ranges for mean (\Delta D) and standard deviation for all voxels
was [-0.3%+/-2.0% to +1.0%+/-12.0%] ([-0.1%+/-4% to +4.0%+/-26%]). Voxel-wise
dice similarity coefficients for 20-130% dose ranged from [0.96, 0.91]([0.94,
0.87]). DVH metric prediction in the training (test) set were HRCTV(\Delta
D_90)=-0.19+/-0.55 Gy (-0.09+/-0.67 Gy), bladder(\Delta D_2cc)=-0.06+/-0.54 Gy
(-0.17+/-0.67 Gy), rectum(\Delta D)_2cc=-0.03+/-0.36 Gy (-0.04+/-0.46 Gy), and
sigmoid(\Delta D_2cc)=-0.01+/-0.34 Gy (0.00+/-0.44 Gy). Conclusion: 3D
knowledge-based dose predictions for T&O brachytherapy provide accurate
voxel-level and DVH metric estimates.
