Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques

Abstract Background Recent studies have shown deep learning techniques are able to predict three‐dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated. Purpose To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer. Methods The dose prediction model was trained with 93 retrospective clinical VMAT plans for patients with lung cancer from our institutional patient database. The prescription doses of these plans ranged from 35 to 72 Gy, with various fractionation schemes. We used a 3D U‐Net architecture to predict 3D dose distributions with 75 plans for training and 18 plans for testing. Model input consisted of computed tomography (CT) images, target and normal tissue contours and prescription doses. We first evaluated model accuracy by comparing the predicted and clinical plan doses for the test set, and then performed replanning according to predicted dose distributions. Furthermore, we evaluated the model prospectively in an additional set of 10 patients from our institution by two approaches where dose prediction was either blinded or provided to treatment planners. We then assessed whether dose prediction could identify suboptimal plan quality and how it affects plan quality if adopted in clinical planning workflow. Results The dose prediction model achieved good agreement between the predicted and clinical plan dose distributions, with a mean dose difference of −0.49 ± 0.54 Gy across the test set. The replanning study guided by dose prediction showed that a small subset of the original plans could benefit from improvements regarding sparing of the spinal cord and esophagus. The analysis of the prospective dataset, with initial and final clinical plans generated in the absence of dose prediction, showed that the predicted doses were able to identify possible improvements of target coverage and normal tissue sparing in the initial plans similar to those made by the final plans for majority of the patients, but in varied magnitudes. Moreover, the plans generated with dose prediction guidance were able to consistently improve normal tissue sparing compared to the plans generated without dose prediction guidance. Conclusions We demonstrated that our deep learning model can consistently predict high quality VMAT lung plans for a variety of prescription doses. The dose prediction tool was also effective in identifying suboptimal plan quality, suggesting its potential benefit in automated treatment planning and evaluation.