Shan, Jie

PurposeCurrently, in clinical practice of intensity‐modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization.Methods and materialsCao et al. have demonstrated a two‐stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings.ResultThe new method achieved better plan quality than the conventional method. The D95% of the clinical target volume (CTV) normalized to the prescription dose was (mean [min–max]): (99.4% [99.2%–99.6%]) vs. (99.2% [98.6%–99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose–volume histogram band gap (narrower band gap means more robust plans) at D95% normalized to the prescription dose was (mean [min–max]): (1.5% [0.5%–4.3%]) vs. (1.2% [0.6%–3.8%]).ConclusionOur new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine‐deliverable plans with better tumor coverage while maintaining high‐plan robustness.

Purpose We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity‐modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs. Methods We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The “worst‐case robust optimization” model was applied. One additional term as a “biological surrogate (BS)” of OARs due to the high LET‐related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose‐averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in‐house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst‐case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited‐memory Broyden–Fletcher–Goldfarb–Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose–volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods. Results For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model. Conclusions Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.

AbstractBackgroundIn treatment planning for intensity‐modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed‐integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose‐volume constraints. In this study, we incorporated dose‐volume constraints in an MIP model to generate treatment plans for IMPT.MethodsWe created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited‐memory Broyden–Fletcher–Goldfarb–Shanno (L‐BFGS) method while the other plan was derived with our MIP model with dose‐volume constraints. We then compared these two plans by dose‐volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose‐volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection.ResultsThe MIP model with dose‐volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial‐and‐error process. Some notable reduction in the mean doses of OARs is observed.ConclusionsThe treatment plans from our MIP model with dose‐volume constraints can meet all dose‐volume constraints for OARs and targets without any tedious trial‐and‐error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.