A robust optimisation genetic algorithm for HDR prostate brachytherapy including all major uncertainties

Objective.In high-dose-rate prostate brachytherapy, uncertainties are likely to cause a deviation from the nominal treatment plan, potentially leading to failure in achieving clinical objectives. Robust optimisation has the potential to maximise the probability that objectives are met during treatment despite these uncertainties.Approach.A probabilistic robust optimiser that incorporating fourteen major uncertainty sources was developed and evaluated on 49 patients. Three objective functions were maximised to generate the approximate Pareto front of 200 robust-optimised plans, approximating the robustness of: (1) The minimum dose to the hottest 90% of the prostate (D90P), (2) The maximum dose to the urethra (D0.01 ccU), and (3) The maximum dose to the rectum (D0.1 ccR). Plans were then robustly evaluated using 1000 uncertainty scenarios each simulating a possible deviation from the planned treatment. The percentage of scenarios meeting theD90P,D0.01 ccU, andD0.1 ccRmetrics were determined, along with the overall pass rate, defined as the percentage of scenarios meting all three metrics simultaneously. These pass-rates, along with nominal metrics, were, were used to select the best robust-optimised plan. A radiation oncologist evaluated the best robust-optimised plans against the treatment planning system (TPS)-optimised plan for ten patients. The same selection criteria were then applied to a further cohort of 39 patients and the same plan comparisons performed.Main results. All best robust-optimised plans had higher overall pass-rates (mean: 50.7 ± 1.5%, SD: 14.2%) then TPS-optimised plans (mean: 32.0 ± 1.5%, SD: 12.3%). The meanD0.01 ccUpass-rate was 66.0 ± 1.3% (SD: 12.1) for the robust-optimised plans compared with 47.2 ± 1.3% (SD: 9.3%) for TPS-optimised plans. TheD90Ppass-rates was higher for robust-optimised plans (mean: 85.6 ± 1.1%, SD: 9.5%) then TPS-optimised (mean: 82.2 ± 1.1%, SD: 13.8%) in 36 patients.D0.1 ccRpass-rates remained consistently high for both optimisation methods.Significance. The robust optimisation algorithm generated plans with greater robustness than the TPS-optimised plans for nine out of ten patients evaluated by a radiation oncologist, in an average algorithm runtime of 1-minute-49 s.