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

Probabilistic Pareto plan generation for semiautomated multicriteria radiation therapy treatment planning