Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer. Issue 4 (28th December 2022) - Yagihashi, Takayuki; Inoue, Kazumasa; Nagata, Hironori; Yamanaka, Masashi; Yamano, Akihiro; Suzuki, Shunsuke; Yamakabe, Wataru; Sato, Naoki; Omura, Motoko; Inoue, Tatsuya
Abstract: Background: Geometrical uncertainties in patients can severely affect the quality of radiotherapy. Purpose: We evaluated the dosimetric efficacy of robust optimization for helical intensity‐modulated radiotherapy (IMRT) planning in the presence of patient setup uncertainty and anatomical changes. Methods: Two helical IMRT plans for 10 patients with localized prostate cancer were created using either minimax robust optimization (robust plan) or a conventional planning target volume (PTV) margin approach (PTV plan). Plan robustness was evaluated by creating perturbed dose plans with setup uncertainty from isocenter shifts and anatomical changes due to organ variation. The magnitudes of the geometrical uncertainties were based on the patient setup uncertainty considered during robust optimization, which was identical to the PTV margin. The homogeneity index, and target coverage (TC, defined as the V100% of the clinical target volume), and organs at risk (OAR; rectum and bladder) doses were analyzed for all nominal and perturbed plans. A statistical t ‐test was performed to evaluate the differences between the robust and PTV plans. Results: Comparison of the nominal plans showed that the robust plans had lower OAR doses and a worse homogeneity index and TC than the PTV plans. The evaluations of robustness that considered setup errors more than the PTV margin demonstrated that the worst‐case perturbed scenarios for robust plans had significantly higher TC while maintaining lower OAR doses. However, when anatomical changes were considered, improvement in TC from robust optimization was not observed in the worst‐case perturbed plans. Conclusions: For helical IMRT planning in localized prostate cancer, robust optimization provides benefits over PTV margin–based planning, including better OAR sparing, and increased robustness against systematic patient‐setup errors.