  Objective: We propose a semiautomatic pipeline for radiation therapy
treatment planning, combining ideas from machine learning-automated planning
and multicriteria optimization (MCO).
  Approach: Using knowledge extracted from historically delivered plans,
prediction models for spatial dose and dose statistics are trained and
furthermore systematically modified to simulate changes in tradeoff priorities,
creating a set of differently biased predictions. Based on the predictions, an
MCO problem is subsequently constructed using previously developed dose
mimicking functions, designed in such a way that its Pareto surface spans the
range of clinically acceptable yet realistically achievable plans as exactly as
possible. The result is an algorithm outputting a set of Pareto optimal plans,
either fluence-based or machine parameter-based, which the user can navigate
between in real time to make adjustments before a final deliverable plan is
created.
  Main results: Numerical experiments performed on a dataset of prostate cancer
patients show that one may often navigate to a better plan than one produced by
a single-plan-output algorithm.
  Significance: We demonstrate the potential of merging MCO and a data-driven
workflow to automate labor-intensive parts of the treatment planning process
while maintaining a certain extent of manual control for the user.
