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Automating the radiotherapy treatment planning process is a technicallychallenging problem. The majority of automated approaches have focused oncustomizing and inferring dose volume objectives to used in plan optimization.In this work we outline a multi-patient atlas-based dose prediction approachthat learns to predict the dose-per-voxel for a novel patient directly from thecomputed tomography (CT) planning scan without the requirement of specifyingany objectives. Our method learns to automatically select the most effectiveatlases for a novel patient, and then map the dose from those atlases onto thenovel patient. We extend our previous work to include a conditional randomfield for the optimization of a joint distribution prior that matches thecomplementary goals of an accurately spatially distributed dose distributionwhile still adhering to the desired dose volume histograms. The resultingdistribution can then be used for inverse-planning with a new spatial doseobjective, or to create typical dose volume objectives for the canonicaloptimization pipeline. We investigated six treatment sites (633 patients fortraining and 113 patients for testing) and evaluated the mean absolutedifference (MAD) in all DVHs for the clinical and predicted dose distribution.The results on average are favorable in comparison to our previous approach(1.91 vs 2.57). Comparing our method with and without atlas-selection furthervalidates that atlas-selection improved dose prediction on average in WholeBreast (0.64 vs 1.59), Prostate (2.13 vs 4.07), and Rectum (1.46 vs 3.29) whileit is less important in Breast Cavity (0.79 vs 0.92) and Lung (1.33 vs 1.27)for which there is high conformity and minimal dose shaping. In CNS Brain,atlas-selection has the potential to be impactful (3.65 vs 5.09), but selectingthe ideal atlas is the most challenging.

Voxel-Based Dose Prediction with Multi-Patient Atlas Selection for Automated Radiotherapy Treatment Planning