  This work aims to study the generalizability of a pre-developed deep learning
(DL) dose prediction model for volumetric modulated arc therapy (VMAT) for
prostate cancer and to adapt the model to three different internal treatment
planning styles and one external institution planning style. We built the
source model with planning data from 108 patients previously treated with VMAT
for prostate cancer. For the transfer learning, we selected patient cases
planned with three different styles from the same institution and one style
from a different institution to adapt the source model to four target models.
We compared the dose distributions predicted by the source model and the target
models with the clinical dose predictions and quantified the improvement in the
prediction quality for the target models over the source model using the Dice
similarity coefficients (DSC) of 10% to 100% isodose volumes and the
dose-volume-histogram (DVH) parameters of the planning target volume and the
organs-at-risk. The source model accurately predicts dose distributions for
plans generated in the same source style but performs sub-optimally for the
three internal and one external target styles, with the mean DSC ranging
between 0.81-0.94 and 0.82-0.91 for the internal and the external styles,
respectively. With transfer learning, the target model predictions improved the
mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles,
respectively. Target model predictions significantly improved the accuracy of
the DVH parameter predictions to within 1.6%. We demonstrated model
generalizability for DL-based dose prediction and the feasibility of using
transfer learning to solve this problem. With 14-29 cases per style, we
successfully adapted the source model into several different practice styles.
This indicates a realistic way to widespread clinical implementation of
DL-based dose prediction.
