Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify subopti-mal plans.Methods and Materials: A total of 245 volumetric modulated arc therapy 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 computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's perfor-mance 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 com-pared with 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% +/- 1.34%, -0.42% +/- 1.27%, and -0.12% +/- 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 +/- 1.40 Gy and -0.96 +/- 2.08 Gy, respectively. For the plan quality assess-ment 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.Conclusions: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for auto-mated, individualized assessment of HN plan quality.(c) 2023 The Authors. Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)