A feasibility study on deep learning‐based individualized 3D dose distribution prediction. Issue 8 (11th July 2021)

• Record Type: Journal Article
• Authors: Ma, Jianhui; Nguyen, Dan; Bai, Ti; Folkerts, Michael; Jia, Xun; Lu, Weiguo; Zhou, Linghong; Jiang, Steve
• Is Part Of: Medical physics. Volume 48:Issue 8(2021)
• Page Start: 4438
• Page End: 4447
• Publication Date: 2021-07-11
• Subjects: deep learning -- dose volume histogram -- Pareto optimal dose distribution prediction -- physicians' preferred trade‐offs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15025
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Radiation therapy treatment planning is a trial‐and‐error, often time‐consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre‐trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient‐specific anatomy but also on physicians' preferred trade‐offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine‐tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade‐offs, as represented by a dose volume histogram (DVH), as inputs. Methods: In this work, we developed a modified U‐Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine‐tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. Results: The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose. Conclusions: In this feasibility study, we have developed a 3D U‐Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.