  Recent works in automated radiotherapy treatment planning have used machine
learning based on historical treatment plans to infer the spatial dose
distribution for a novel patient directly from the planning image. We present
an atlas-based approach which learns a dose prediction model for each patient
(atlas) in a training database, and then learns to match novel patients to the
most relevant atlases. The method creates a spatial dose objective, which
specifies the desired dose-per-voxel, and therefore replaces any requirement
for specifying dose-volume objectives for conveying the goals of treatment
planning. A probabilistic dose distribution is inferred from the most relevant
atlases, and is scalarized using a conditional random field to determine the
most likely spatial distribution of dose to yield a specific dose prior
(histogram) for relevant regions of interest. Voxel-based dose mimicking then
converts the predicted dose distribution to a deliverable treatment plan dose
distribution. In this study, we investigated automated planning for right-sided
oropharaynx head and neck patients treated with IMRT and VMAT. We compare four
versions of our dose prediction pipeline using a database of 54 training and 12
independent testing patients. Our preliminary results are promising; automated
planning achieved a higher number of dose evaluation criteria in 7 patients and
an equal number in 4 patients compared with clinical. Overall, the relative
number of criteria achieved was higher for automated planning versus clinical
(17 vs 8) and automated planning demonstrated increased sparing for organs at
risk (52 vs 44) and better target coverage/uniformity (41 vs 31).
