Automatically configuring the reference point method for automated multi-objective treatment planning

Automated treatment planning algorithms have demonstrated capability in generating consistent and high-quality treatment plans. Their configuration (i.e. determining the algorithm’s parameters), however, can be a labour-intensive and time-consuming trial-and-error procedure. Previously, we introduced the reference point method (RPM) for fast automated multi-objective treatment planning. The RPM generates a single Pareto optimal plan for each patient. When the RPM is configured appropriately, this plan has clinically favourable trade-offs between all plan objectives. This paper proposes a new procedure to automatically generate a single configuration of the RPM per tumour site. The procedure was tested for prostate cancer. Planning CT scans of 287 previously treated patients were included in a database, together with corresponding Pareto optimal plans generated using our clinically applied two-phase -constraint method (part of Erasmus-iCycle) for automated multi-objective treatment planning. The procedure developed acquires plan characteristics observed in a training set. Based on these, an RPM configuration is automatically generated according to user preferences which specify acceptable differences between training set plans and corresponding RPM generated plans. For example, compared to the training set plans, the RPM generated plans need to have similar PTV coverage, and preferably reduced high rectum dose while slight deteriorations in other objectives are allowed. Training sets of different sizes were tested, and the quality of the resulting RPM configurations was evaluated on the test set (subset of the database not used for training). Using the new procedure, an RPM configuration was generated for each training set. The quality of RPM generated plans was similar or slightly better than that of the corresponding test set plans. The proposed automated configuration procedure greatly reduces the manual configuration workload, and thereby improves the efficiency and effectiveness of an automated clinical treatment planning workflow

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