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The use of neural networks to directly predict three-dimensional dosedistributions for automatic planning is becoming popular. However, the existingmethods only use patient anatomy as input and assume consistent beamconfiguration for all patients in the training database. The purpose of thiswork is to develop a more general model that, in addition to patient anatomy,also considers variable beam configurations, to achieve a more comprehensiveautomatic planning with a potentially easier clinical implementation, withoutthe need of training specific models for different beam settings.

Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations