Poel, R.; Kamath, A.J.; Willmann, J.; Andratschke, N.; Ermiş, E.; Aebersold, D.M.; Manser, P.; Reyes, M. Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring. Cancers2023, 15, 4226.
Poel, R.; Kamath, A.J.; Willmann, J.; Andratschke, N.; Ermiş, E.; Aebersold, D.M.; Manser, P.; Reyes, M. Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring. Cancers 2023, 15, 4226.
Poel, R.; Kamath, A.J.; Willmann, J.; Andratschke, N.; Ermiş, E.; Aebersold, D.M.; Manser, P.; Reyes, M. Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring. Cancers2023, 15, 4226.
Poel, R.; Kamath, A.J.; Willmann, J.; Andratschke, N.; Ermiş, E.; Aebersold, D.M.; Manser, P.; Reyes, M. Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring. Cancers 2023, 15, 4226.
Abstract
Background: External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of the planning procedure, machine learning predictions of the dose distributions have been introduced to speed up the planning procedure and to serve as quality assurance. The most recent dose prediction models are based on deep learning U-Nets that give good approximations of the dose in 3D almost instantly. It is our purpose to train a 3D dose prediction for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. Methods: From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a concatenated 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes according to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases.Results: The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy.Conclusions: We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness, by targeted updating the training set, making it a useful technique for dose awareness in the contouring evaluation and quality assurance.
Keywords
Radiotherapy; Dose Prediction; Deep Learning; Quality Assurance; VMAT; Glioblastoma
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
