  Objective: Machine learning (ML) based radiation treatment (RT) planning
addresses the iterative and time-consuming nature of conventional inverse
planning. Given the rising importance of Magnetic resonance (MR) only treatment
planning workflows, we sought to determine if an ML based treatment planning
model, trained on computed tomography (CT) imaging, could be applied to MR
through domain adaptation. Methods: In this study, MR and CT imaging was
collected from 55 prostate cancer patients treated on an MR linear accelerator.
ML based plans were generated for each patient on both CT and MR imaging using
a commercially available model in RayStation 8B. The dose distributions and
acceptance rates of MR and CT based plans were compared using institutional
dose-volume evaluation criteria. The dosimetric differences between MR and CT
plans were further decomposed into setup, cohort, and imaging domain
components. Results: MR plans were highly acceptable, meeting 93.1% of all
evaluation criteria compared to 96.3% of CT plans, with dose equivalence for
all evaluation criteria except for the bladder wall, penile bulb, small and
large bowel, and one rectum wall criteria (p<0.05). Changing the input imaging
modality (domain component) only accounted for about half of the dosimetric
differences observed between MR and CT plans. Anatomical differences between
the ML training set and the MR linac cohort (cohort component) were also a
significant contributor. Significance: We were able to create highly acceptable
MR based treatment plans using a CT-trained ML model for treatment planning,
although clinically significant dose deviations from the CT based plans were
observed.
