  Purpose: We propose a general framework for quantifying predictive
uncertainties of dose-related quantities and leveraging this information in a
dose mimicking problem in the context of automated radiation therapy treatment
planning. Methods: A three-step pipeline, comprising feature extraction, dose
statistic prediction and dose mimicking, is employed. In particular, the
features are produced by a convolutional variational autoencoder and used as
inputs in a previously developed nonparametric Bayesian statistical method,
estimating the multivariate predictive distribution of a collection of
predefined dose statistics. Specially developed objective functions are then
used to construct a dose mimicking problem based on the produced distributions,
creating deliverable treatment plans. Results: The numerical experiments are
performed using a dataset of 94 retrospective treatment plans of prostate
cancer patients. We show that the features extracted by the variational
autoencoder captures geometric information of substantial relevance to the dose
statistic prediction problem, that the estimated predictive distributions are
reasonable and outperforms a benchmark method, and that the deliverable plans
agree well with their clinical counterparts. Conclusions: We demonstrate that
prediction of dose-related quantities may be extended to include uncertainty
estimation and that such probabilistic information may be leveraged in a dose
mimicking problem. The treatment plans produced by the proposed pipeline
resemble their original counterparts well, illustrating the merits of a
holistic approach to automated planning based on probabilistic modeling.
