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Automatic configuration of the reference point method for fully automated multi‐objective treatment planning applied to oropharyngeal cancer
Author(s) -
van Haveren Rens,
Heijmen Ben J. M.,
Breedveld Sebastiaan
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14073
Subject(s) - radiation treatment planning , pareto principle , computer science , point (geometry) , quality assurance , set (abstract data type) , mathematical optimization , algorithm , artificial intelligence , mathematics , medicine , radiation therapy , surgery , engineering , operations management , geometry , external quality assessment , programming language
Purpose In automated treatment planning, configuration of the underlying algorithm to generate high‐quality plans for all patients of a particular tumor type can be a major challenge. Often, a time‐consuming trial‐and‐error tuning procedure is required. The purpose of this paper is to automatically configure an automated treatment planning algorithm for oropharyngeal cancer patients. Methods Recently, we proposed a new procedure to automatically configure the reference point method (RPM), a fast automatic multi‐objective treatment planning algorithm. With a well‐tuned configuration, the RPM generates a single Pareto optimal treatment plan with clinically favorable trade‐offs for each patient. The automatic configuration of the RPM requires a set of computed tomography (CT) scans with corresponding dose distributions for training. Previously, we demonstrated for prostate cancer planning with 12 objectives that training with only 9 patients resulted in high‐quality configurations. This paper further develops and explores the new automatic RPM configuration procedure for head and neck cancer planning with 22 objectives. Investigations were performed with planning CT scans of 105 previously treated unilateral or bilateral oropharyngeal cancer patients together with corresponding Pareto optimal treatment plans. These plans were generated with our clinically applied two‐phase ε ‐constraint method (Erasmus‐iCycle) for automated multi‐objective treatment planning, ensuring consistent high quality and Pareto optimality of all plans. Clinically relevant, nonconvex criteria, such as dose‐volume parameters and NTCPs, were included to steer the RPM configuration. Results Training sets with 20–50 patients were investigated. Even with 20 training plans, high‐quality configurations of the RPM were feasible. Automated plan generation with the automatically configured RPM resulted in Pareto optimal plans with overall similar or better quality than that of the Pareto optimal database plans. Conclusions Automatic configuration of the RPM for automated treatment planning is feasible and drastically reduces the time and workload required when compared to manual tuning of an automated treatment planning algorithm.