  Purpose: This study aimed to use deep learning-based dose prediction to
assess head and neck (HN) plan quality and identify suboptimal plans.
  Methods: A total of 245 VMAT HN plans were created using RapidPlan
knowledge-based planning (KBP). A subset of 112 high-quality plans was selected
under the supervision of an HN radiation oncologist. We trained a 3D Dense
Dilated U-Net architecture to predict 3-dimensional dose distributions using
3-fold cross-validation on 90 plans. Model inputs included CT images, target
prescriptions, and contours for targets and organs at risk (OARs). The model's
performance was assessed on the remaining 22 test plans. We then tested the
application of the dose prediction model for automated review of plan quality.
Dose distributions were predicted on 14 clinical plans. The predicted versus
clinical OAR dose metrics were compared to flag OARs with suboptimal normal
tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR
flags were compared to manual flags by 3 HN radiation oncologists.
  Results: The predicted dose distributions were of comparable quality to the
KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and
D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and
-0.12%(SD=1.97%), respectively, and the OAR mean and maximum doses were within
-0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality assessment
study, radiation oncologists flagged 47 OARs for possible plan improvement.
There was high interphysician variability; 83% of physician-flagged OARs were
flagged by only one of 3 physicians. The comparative dose prediction model
flagged 63 OARs, including 30 of 47 physician-flagged OARs.
  Conclusion: Deep learning can predict high-quality dose distributions, which
can be used as comparative dose distributions for automated, individualized
assessment of HN plan quality.
