A generalizable dose prediction model for automatic radiotherapy planning based on physics‐informed priors and large‐kernel convolutions

We have developed a generalizable and accurate dose prediction model capable of predicting dose distributions for diverse tumor sites, delivery techniques, and prescription doses. The integration of physics-informed priors and large-kernel convolutions significantly enhanced prediction accuracy. The feasibility of universal automatic planning based on the generalizable dose prediction model has been demonstrated, highlighting its potential to facilitate broader clinical application of automatic planning and to improve planning efficiency, consistency, and quality. The code and trained models are publicly available at https://github.com/tyxiong123/RTPDosePred.