Characterization of automatic treatment planning approaches in radiotherapy

Abstract

Background and purpose: Automatic approaches are widely implemented to automate dose optimization in radiotherapy treatment planning. This study systematically investigates how to configure automatic planning in order to create the best possible plans. Materials and methods: Automatic plans were generated using protocol based automatic iterative optimization. Starting from a simple automation protocol which consisted of the constraints for targets and organs at risk (OAR), the performance of the automatic approach was evaluated in terms of target coverage, OAR sparing, conformity, beam complexity, and plan quality. More complex protocols were systematically explored to improve the quality of the automatic plans. The protocols could be improved by adding a dose goal on the outer 2 mm of the PTV, by setting goals on strategically chosen subparts of OARs, by adding goals for conformity, and by limiting the leaf motion. For prostate plans, development of an automated post-optimization procedure was required to achieve precise control over the dose distribution. Automatic and manually optimized plans were compared for 20 head and neck (H&N), 20 prostate, and 20 rectum cancer patients. Results: Based on simple automation protocols, the automatic optimizer was not always able to generate adequate treatment plans. For the improved final configurations for the three sites, the dose was lower in automatic plans compared to the manual plans in 12 out of 13 considered OARs. In blind tests, the automatic plans were preferred in 80% of cases. Conclusions: With adequate, advanced, protocols the automatic planning approach is able to create high-quality treatment plans.

Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality

“…Furthermore, a prospective analysis on a larger patient cohort is suggested. Indeed, Fogliata et al highlighted those systematic investigations are needed to test the performance and robustness of the automated tools [9], and Wortel et al pointed out the importance of periodically checking the quality and the acceptance rates of automatic plans after their clinical introduction [40].…”

Explaining the dosimetric impact of contouring errors in head and neck radiotherapy

“…In many cases, the optimization of the planning system turns out not to be very sensitive to the exact shape of a contour, since decreasing the fluence from a certain beam angle decreases OAR dose everywhere in the beams-eyeview projection of the OAR. Therefore, to first order, for each beam angle, only changes in the contour that change the beams-eye-view projection will substantially influence the optimized fluence [10,11].…”

Full daily re-optimization improves plan quality during online adaptive radiotherapy

“…Although not yet widely implemented in a clinical setting, automated treatment planning may be highly beneficial as it has been shown to be time efficient and created highly standardized planning results [26] . Thus, using automated treatment planning for online adaptive radiotherapy has the advantage of efficient planning and eliminates inter-planner variability [27] , [28] , [29] .…”