title: Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy.
abstract: OBJECTIVE
  To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy.
  
  
  METHODS
  Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12-30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = Dclin - Dpred. The mean (〈δDr〉), standard deviation (σδDr ), and their interquartile range (IQR) for the training plans were evaluated at a 2-3 mm interval from the PTV boundary (rPTV) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model.
  
  
  RESULTS
  The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from -1% to 0% with maximum IQR of 3% over rPTV ∈ [ - 6, 30] mm. The average prediction error was less than 10% for the same rPTV range. For SRS cases, the average prediction bias ranged from -0.7% to 1.5% with maximum IQR of 5% over rPTV ∈ [ - 4, 32] mm. The average prediction error was less than 8%. Four potentially suboptimal plans were identified for each site and subsequent replanning demonstrated improved sparing of rectum and brainstem.
  
  
  CONCLUSIONS
  The study demonstrates highly accurate knowledge-based 3D dose predictions for radiotherapy plans.
authors.affiliation: Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California 92093.
authors.forename: Satomi
authors.initials: S
authors.lastname: Shiraishi
authors.affiliation: Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California 92093.
authors.forename: Kevin L
authors.initials: KL
authors.lastname: Moore
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated