Purpose: To predict high-quality, realistic dose distributions for head and neck (HN) VMAT plans that can be used as comparative dose distributions for automated peer review of plan quality.
Methods: We trained a 3D dilated dense-UNet to predict 3D dose distributions for HN patients. The architecture takes a 24-channel input including CT images, target prescriptions, and normal tissue and target contours. 118 VMAT HN plans with 2-3 dose levels were split into train, cross-validation, and test datasets (3:1:1 respectively). The model was trained on random patches using a custom weighted mean squared error loss function, which assigns a customized weight to each contour. The predicted dose distributions were assessed by a HN radiation oncologist using a 5-point scale (1:No edits needed, 2:Minor edits optional, 3:Minor edits needed; 4:Major edits needed, 5:Fails to meet clinical criteria). A score of 1-3 indicates clinical acceptability.
Results: The model predicted dose distributions of comparable plan quality to the clinical plans. The mean dose difference between the predicted and clinical dose distributions of the testing dataset was -0.42±0.28Gy. On average, the predicted target DVH metrics of D1, D95, and D99 normalized to the prescription were within -1.94±1.35%, -0.12±3.55%, and 0.64±6.23% of the clinical plans, respectively. The predicted organ at risk metrics of mean and max doses were within -0.35±1.41Gy and -0.83±1.85Gy of the clinical plans, respectively. The predicted dose distributions on the test set were scored as clinically acceptable for 18/21 patients (score 1:5 patients, score 2:9 patients, score 3:4 patients). The other 3 patients received a score of 4 for poor target coverage and target dose fall-off.
Conclusion: The 3D dilated dense-UNet can predict high-quality, realistic dose distributions for HN VMAT plans. Physician assessment indicates that greater than 85% of predictions are clinically acceptable, supporting the use of dose prediction to QA plan quality.
Funding Support, Disclosures, and Conflict of Interest: Mary Gronberg was supported by funding from the CCTS TL1 Program and the American Legion Auxiliary.
TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation