Jiasen Ma

Purpose: This study aimed to compare long-term patient-reported outcomes in bowel and urinary domains between intensity-modulated radiotherapy (IMRT) and intensity-modulated proton therapy (IMPT) for localized prostate cancer. Methods and Materials: Patients with clinical T1–T2 prostate cancer receiving IMRT or IMPT at a tertiary cancer center from 2015–2018 were analyzed to determine the changes in the prospectively collected bowel function (BF), urinary irritative/obstructive symptoms (UO), and urinary incontinence (UI) domains of EPIC-26. The mean changes in EPIC-26 scores were evaluated from pretreatment to 24 months post-radiotherapy for each modality. A score change >50% of the baseline standard deviation was considered a clinically meaningful change. Results: A total of 82 patients treated with IMRT (52.2%) and 56 patients treated with IMPT (53.3%) completed the questionnaire at baseline and 24 months post-RT. There were no baseline differences in domain scores between treatment modalities. At 24 months post-radiotherapy, there was a significant and clinically meaningful decline in the BF mean score in the IMRT cohort (−4.52 (range −50, 29.17), p = 0.003), whereas the decline in BF score did not reach clinical relevance or significance (−1.88 (range −37.5, 50), p = 0.046) when accounting for the Bonferroni adjustment in the IMPT cohort. A higher proportion of patients treated with IMRT had a clinically relevant reduction in BF when compared with IMPT (47.37% vs. 25.93%, p = 0.017). The mean changes in the UI and UO scores of the IMRT and IMPT cohorts were neither statically significant nor clinically relevant. Conclusions: IMPT leads to a smaller decrease in BF than IMRT at 24 months post-RT, while there was no differential effect on UO and UI.

To develop a Monte Carlo (MC)-based and robust ion beam therapy optimization system that separates the optimization algorithm from the relative biological effectiveness (RBE) modeling. Robustly optimized dose distributions were calculated and compared across three ion therapy beams (proton, helium, carbon). The effect of different averaging techniques in calculating RBE in mixed beams was also investigated. Ion particles were transported in TOPAS MC. The microdosimetric-kinetic model (MKM) parameter, saturation corrected specific energy ( z ∗ ), was calculated with a customized MKM implementation in TOPAS MC. Intensity modulated ion therapy robust optimization was performed by a quasi-Newton iterative method based on dose-volume objective function. The robust optimization took setup and range uncertainties into account. In the present work, the biological dose for each individual spot was calculated, and then summed together to calculate total biological dose. In other published works, radiosensitive parameters, such as z ∗ , were first averaged over all beam spots within a mixed-beam field, after which biological dose was calculated using the averaged radiosensitive parameters. The difference between the two mixed-beam biological dose calculations was quantified. Robust plans were achieved with the three particle types. The effect of averaging z ∗ depended on particle type. The difference between biological doses calculated with individual z ∗ and averaged z ∗ may be greater than 3% for a carbon beam. MC based radiobiological and robust optimization was made flexible to incorporate dose-volume histogram constraints and to be independent of RBE models. Iterative optimization with RBE models was feasible. Evaluation of the RBE calculation for mixed beam could be necessary if better accuracy was demanded.

Purpose: Accuracy of dose calculation models and robustness under various uncertainties are key factors influencing the quality of intensity modulated proton therapy (IMPT) plans. To mitigate the effects of uncertainties and to improve the dose calculation accuracy, an all‐scenario robust IMPT optimization based on accurate Monte Carlo (MC) dose calculation was developed.Methods: In the all‐scenario robust IMPT optimization, dose volume histograms (DVHs) were computed for the nominal case and for each uncertainty scenario. All scenarios were weighted by DVH values dynamically throughout optimization iterations. In contrast, probabilistic approach weighted scenarios with fixed scenario weights and worst case optimizations picked one single scenario — the worst scenario for each iteration. Uncertainties in patient setup and proton range were considered in all clinical cases studied. Graphics processing unit (GPU) computation was employed to reduce the computational time in both the MC dose calculation and optimization stages. A previously published adaptive quasi‐Newton method for proton optimization was extended to include robustness. To validate the all‐scenario algorithm extension, it was compared with the single scenario optimization target volume (OTV) based approach in clinical cases of three different disease sites. Additional comparisons with worst case optimization methods were conducted to evaluate the performance of the all‐scenario robust optimization against other robust optimizations.Results: The all‐scenario robust IMPT optimization spared organs at risk (OARs) better than the OTV‐based method while maintaining target coverage and improving the robustness of targets and OARs. Compared with composite and voxel‐wise worst case optimization, the all‐scenario robust optimization converged faster, and arrived at solutions of tighter DVH robustness spread, better target coverage and lower OAR dose.Conclusion: An all‐scenario robust IMPT treatment planning system was developed using an adaptive quasi‐Newton optimization method. The optimization system was GPU‐accelerated and based on MC dose calculation. Improved performance was observed in clinical cases when compared with worst case optimization methods.

Purpose: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC‐based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation.Methods: An in‐house graphics processing unit (GPU)‐based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least‐squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi‐GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work.Results: For relatively large and complex three‐field head and neck cases, i.e., >100 000 spots with a target volume of ∼1000 cm³ and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in‐house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons.Conclusions: A MC‐based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45 000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.