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Purpose  Stereotactic body radiation therapy ( SBRT ) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge‐based artificial neural network dose models ( ANN ‐ DM s) to predict dose distributions that would be approved by experienced physicians.    Methods  Arc‐based  SBRT  treatment plans for 43 pancreatic cancer patients were planned, delivering 30–33 Gy in five fractions. Treatments were overseen by one of two physicians with individual treatment approaches, with variations in prescribed dose, target volume delineation, and primary organs at risk. Using dose distributions calculated by a commercial treatment planning system ( TPS ), physician‐approved treatment plans were used to train  ANN ‐ DM s that could predict physician‐approved dose distributions based on a set of geometric parameters (vary from voxel to voxel) and plan parameters (constant across all voxels for a given patient). Patient datasets were randomly allocated, with two‐thirds used for training, and one‐third used for validation. Differences between  TPS  and  ANN ‐ DM  dose distributions were used to evaluate model performance.  ANN ‐ DM  design, including neural network structure and parameter choices, was evaluated to optimize dose model performance.    Results  Remarkable improvements in  ANN ‐ DM  accuracy (i.e., from > 30% to < 5% mean absolute dose error, relative to the prescribed dose) were achieved by training separate dose models for the treatment style of each physician. Increased neural network complexity (i.e., more layers, more neurons per layer) did not improve dose model accuracy. Mean dose errors were less than 5% at all distances from the  PTV , and mean absolute dose errors were on the order of 5%, but no more than 10%. Dose–volume histogram errors (in cm 3 ) demonstrated good model performance above 25 Gy, but much larger errors were seen at lower doses.    Conclusions   ANN ‐ DM  dose distributions showed excellent overall agreement with  TPS  dose distributions, and accuracy was substantially improved when each physician's treatment approach was taken into account by training their own dedicated models. In this manner, one could feasibly train  ANN ‐ DM s that could predict the dose distribution desired by a given physician for a given treatment site.

Neural network dose models for knowledge‐based planning in pancreatic SBRT