Probabilistic Pareto plan generation for semiautomated multicriteria radiation therapy treatment planning

TL;DR

This paper introduces a semiautomatic, data-driven pipeline for radiation therapy planning that combines machine learning predictions with multicriteria optimization to generate a set of Pareto optimal plans, improving plan quality and user control.

Contribution

It presents a novel method integrating machine learning and multicriteria optimization to produce a diverse set of clinically acceptable radiation therapy plans.

Findings

Navigation to better plans than single-output algorithms

Effective merging of MCO with data-driven workflows

Potential to automate labor-intensive planning steps

Abstract

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.