Purpose To develop a knowledge‐based automated planning pipeline that generates
treatment plans without feature engineering, using deep neural network architectures for
predicting three‐dimensional (3D) dose. Methods Our knowledge‐based automated
planning (KBAP) pipeline consisted of a knowledge‐based planning (KBP) method that
predicts dose for a contoured computed tomography (CT) image followed by two
optimization models that learn objective function weights and generate fluence‐based …

We develop a knowledge-based automated planning (KBAP) pipeline that generates
treatment plans using deep neural network architectures for predicting 3D doses. Our
pipeline consisted of a generative adversarial network (GAN) to predict dose from a CT
image followed by two optimization models to learn objective function weights and generate
fluence-based plans, respectively. We investigated three different GAN models. The first two
models predicted dose for each axial slice independently. One predicted dose as a RGB …