Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning

Background and purpose: Fast, high-quality deep learning (DL) prediction of patient-specific 3D dose distributions can enable instantaneous treatment planning (IP), in which the treating physician can evaluate the dose and approve the plan immediately after contouring, rather than days later. This would greatly benefit clinical workload, patient waiting times and treatment quality. IP requires that predicted dose distributions closely match the ground truth. This study examines how training dataset size and model size affect dose prediction accuracy for Erasmus-iCycle GT plans to enable IP. Materials and methods: For 1250 prostate patients, dose distributions were automatically generated using Erasmus-iCycle. Hierarchically Densely Connected U-Nets with 2/3/4/5/6 pooling layers were trained with datasets of 50/100/250/500/1000 patients, using a validation set of 100 patients. A fixed test set of 150 patients was used for evaluations. Results: For all model sizes, prediction accuracy increased with the number of training patients, without levelling off at 1000 patients. For 4–6 level models with 1000 training patients, prediction accuracies were high and comparable. For 6 levels and 1000 training patients, the median prediction errors and interquartile ranges for PTV V_95%, rectum V_75Gy and bladder V_65Gy were 0.01 [-0.06,0.15], 0.01 [-0.20,0.29] and −0.02 [-0.27,0.27] %-point. Dose prediction times were around 1.2 s. Conclusion: Although even for 1000 training patients there was no convergence in obtained prediction accuracy yet, the accuracy for the 6-level model with 1000 training patients may be adequate for the pursued instantaneous planning, which is subject of further research.