  Deep learning (DL) 3D dose prediction has recently gained a lot of attention.
However, the variability of plan quality in the training dataset, generated
manually by planners with wide range of expertise, can dramatically effect the
quality of the final predictions. Moreover, any changes in the clinical
criteria requires a new set of manually generated plans by planners to build a
new prediction model. In this work, we instead use consistent plans generated
by our in-house automated planning system (named ``ECHO'') to train the DL
model. ECHO (expedited constrained hierarchical optimization) generates
consistent/unbiased plans by solving large-scale constrained optimization
problems sequentially. If the clinical criteria changes, a new training data
set can be easily generated offline using ECHO, with no or limited human
intervention, making the DL-based prediction model easily adaptable to the
changes in the clinical practice. We used 120 conventional lung patients (100
for training, 20 for testing) with different beam configurations and trained
our DL-model using manually-generated as well as automated ECHO plans. We
evaluated different inputs: (1) CT+(PTV/OAR)contours, and (2) CT+contours+beam
configurations, and different loss functions: (1) MAE (mean absolute error),
and (2) MAE+DVH (dose volume histograms). The quality of the predictions was
compared using different DVH metrics as well as dose-score and DVH-score,
recently introduced by the AAPM knowledge-based planning grand challenge. The
best results were obtained using automated ECHO plans and CT+contours+beam as
training inputs and MAE+DVH as loss function.
