Automation of robust Pareto front-based radiotherapy treatment planning for prostate cancer patients
Sammanfattning: Purpose/objective The main objective was to create a software that automates the process of creating treatment plans used for Pareto front-based dose planning for prostate cancer patients. A second objective/purpose was to add a robustness test to this program to evaluate the effect of prostate movements on the treatment plans. Material/method An IronPython program was designed to control and collect information from the treatment planning system (TPS) RayStation 5 and by using built-in libraries made by the creators of the Raystation 5. The patients selected were prostate cancer patients with treatment of only the prostate, not including nearby lymph nodes or seminal vesicles. Hypo-fractionation treatment plans were created with a prescription of seven fractions of 6.1Gy with a total dose of 42.7Gy, one fraction every other day. A comparison was made between a treatment plan created by a dose planner and Pareto fronts extracted from 1440 treatment plans automatically generated with the plan generation program. The robustness test was evaluated on one patient by using an isocenter shift of (x, y, z) = (0.45cm, 0.02cm, -0.58cm). Result The software consisted of two programs. The first program used the optimizer and dose calculator in RayStation 5 to create deliverable treatment plans. It inputted different objective functions and/or weights to the optimizer. For each change, the optimizer would find a new optimal treatment plan. It saved DVH-data for evaluation of the plans. A built-in robustness test was added to the program to test the effect of prostate movements on the treatment plans it constructed. It moved the finished treatment plan’s isocenter and the dose difference was calculated. The second program, created for evaluation, loaded the saved data from the program generating the treatment plans. In the program, multiple data sets was loaded and compared. It visualized both the Pareto fronts based on the collected plans and all the dose volume histograms (DVHs) for one plan at a time (for a selected plan from the first graph). The program could determine if treatment plans were Pareto optimal and/or clinically acceptable. It was also used to visualize the robustness test, where the static treatment plans and treatment plans with a moved isocenter were plotted in the same graph. The programs automated the process and reduced the work needed to only some preparation before starting the program. The time to create treatment plans for a Pareto front was greatly reduced, as the program could save one plan every 2-3 minutes. When comparing a Pareto front consisting of automatically generated treatment plans and a plan created by a planner, the dose planner’s treatment plan ended up near the Pareto front in all cases. Discussion Prostate cancer was selected for this study due to the fact that it is a comparably simple case involving only a few OARs. The only trade-off that needed to be visualized is the one between the target and the rectum. Thus, only the rectum goal needs to be changed to be able to show the trade-off. If more OARs would be added, it would have taken longer time to generate enough treatment plans to represent the trade-offs. There are, however, improvements that could be made. For further automation, and to decrease the generation time, machine learning could be used. The robustness test was able to show how the dose distribution would be affected by an isocenter movement. Several improvements could be made. For instance, there are several fractions in a treatment course, and the same movement does not occur each time. There might be a continuous movement during the treatment delivery, not only one big movement. Furthermore, the data used was for a 30-minute interval, while in reality, a patient has come and gone in less than half that time. Conclusion A program has been developed and implemented that can be used for automation of creation of treatment plans used for Pareto front-based dose planning. A robustness test was built-in to allow for comparison between the created plans with respect to how much prostate motion would affect them. Features such as machine learning would be a good tool to further automate the process and to reduce the generation time.
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