  The DeepDoseNet 3D dose prediction model based on ResNet and Dilated DenseNet
is proposed. The 340 head-and-neck datasets from the 2020 AAPM OpenKBP
challenge were utilized, with 200 for training, 40 for validation, and 100 for
testing. Structures include 56Gy, 63Gy, 70Gy PTVs, and brainstem, spinal cord,
right parotid, left parotid, larynx, esophagus, and mandible OARs. Mean squared
error (MSE) loss, mean absolute error (MAE) loss, and MAE plus dose-volume
histogram (DVH) based loss functions were investigated. Each model's
performance was compared using a 3D dose score, $\bar{S_{D}}$, (mean absolute
difference between ground truth and predicted 3D dose distributions) and a DVH
score, $\bar{S_{DVH}}$ (mean absolute difference between ground truth and
predicted dose-volume metrics).Furthermore, DVH metrics Mean[Gy] and D0.1cc
[Gy] for OARs and D99%, D95%, D1% for PTVs were computed. DeepDoseNet with the
MAE plus DVH-based loss function had the best dose score performance of the
OpenKBP entries. MAE+DVH model had the lowest prediction error (P<0.0001,
Wilcoxon test) on validation and test datasets (validation:
$\bar{S_{D}}$=2.3Gy, $\bar{S_{DVH}}$=1.9Gy; test: $\bar{S_{D}}$=2.0Gy,
$\bar{S_{DVH}}$=1.6Gy) followed by the MAE model (validation:
$\bar{S_{D}}$=3.6Gy, $\bar{S_{DVH}}$=2.4Gy; test: $\bar{S_{D}}$=3.5Gy,
$\bar{S_{DVH}}$=2.3Gy). The MSE model had the highest prediction error
(validation: $\bar{S_{D}}$=3.7Gy, $\bar{S_{DVH}}$=3.2Gy; test:
$\bar{S_{D}}$=3.6Gy, $\bar{S_{DVH}}$=3.0Gy). No significant difference was
found among models in terms of Mean [Gy], but the MAE+DVH model significantly
outperformed the MAE and MSE models in terms of D0.1cc[Gy], particularly for
mandible and parotids on both validation (P<0.01) and test (P<0.0001) datasets.
MAE+DVH outperformed (P<0.0001) in terms of D99%, D95%, D1% for targets.
MAE+DVH reduced $\bar{S_{D}}$ by ~60% and $\bar{S_{DVH}}$ by ~70%.
