Objective. Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed clinical target distribution (CTD) is an alternative to the conventional clinical target volume (CTV), with initial promise. Previously, using theCTDin planning has primarily been evaluated in comparison to a conventionally defined CTV. Wepropose to compare theCTDapproach against CTVmargins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant. Approach. First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with theCTDapproach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both one- and three-dimensional phantoms, we compare a set of CTDplans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTVmargin size. Main results. The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, theCTDis better at maintaining sufficiently high dose toward the center of the target. Significance. Weconclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.

Objective. Online adaptive radiation therapy requires fast and automated contouring of daily scans for treatment plan re-optimization. However, automated contouring is imperfect and introduces contour uncertainties. This work aims at developing and comparing robust optimization strategies accounting for such uncertainties. Approach. A deep-learning method was used to predict the uncertainty of deformable image registration, and to generate a finite set of daily contour samples. Ten optimization strategies were compared: two baseline methods, five methods that convert contour samples into voxel-wise probabilities, and three methods accounting explicitly for contour samples as scenarios in robust optimization. Target coverage and organ-at-risk (OAR) sparing were evaluated robustly for simplified proton therapy plans for five head-and-neck cancer patients. Results. We found that explicitly including target contour uncertainty in robust optimization provides robust target coverage with better OAR sparing than the baseline methods, without increasing the optimization time. Although OAR doses first increased when increasing target robustness, this effect could be prevented by additionally including robustness to OAR contour uncertainty. Compared to the probability-based methods, the scenario-based methods spared the OARs more, but increased integral dose and required more computation time. Significance. This work proposed efficient and beneficial strategies to mitigate contour uncertainty in treatment plan optimization. This facilitates the adoption of automatic contouring in online adaptive radiation therapy and, more generally, enables mitigation also of other sources of contour uncertainty in treatment planning.

BACKGROUND: The steep dose gradients obtained with pencil beam scanning allow for precise targeting of the tumor but come at the cost of high sensitivity to uncertainties. Robust optimization is commonly applied to mitigate uncertainties in density and patient setup, while its application to motion management, called 4D-robust optimization (4DRO), is typically accompanied by other techniques, including gating, breath-hold, and re-scanning. In particular, current commercial implementations of 4DRO do not model the interplay effect between the delivery time structure and the patient's motion.

PURPOSE: Interplay-robust optimization (IPRO) has previously been proposed to explicitly model the interplay-affected dose during treatment planning. It has been demonstrated that IPRO can mitigate the interplay effect given the uncertainty in the patient's breathing frequency. In this study, we investigate and evaluate IPRO in the context where the motion uncertainty is extended to also include variations in breathing amplitude.

METHODS: The compared optimization methods are applied and evaluated on a set of lung patients. We model the patients' motion using synthetic 4D computed tomography (s4DCT), each created by deforming a reference CT based on a motion pattern obtained with 4D magnetic resonance imaging. Each (s4DCT) contains multiple breathing cycles, partitioned into two sets for scenario generation: one for optimization and one for evaluation. Distinct patient motion scenarios are then created by randomly concatenating breathing cycles varying in period and amplitude. In addition, a method considering a single breathing cycle for generating optimization scenarios (IPRO-1C) is developed to investigate to which extent robustness can be achieved with limited information. Both IPRO and IPRO-1C were investigated with 9, 25, and 49 scenarios.

RESULTS: For all patient cases, IPRO and IPRO-1C increased the target coverage in terms of the near-worst-case (5th percentile) CTV D98, compared to 4DRO. After normalization of plan doses to equal target coverage, IPRO with 49 scenarios resulted in the greatest decreases in OAR dose, with near-worst-case (95th percentile) improvements averaging 4.2 %. IPRO-1C with 9 scenarios, with comparable computational demands as 4DRO, decreased OAR dose by 1.7 %.

CONCLUSIONS: The use of IPRO could lead to more efficient mitigation of the interplay effect, even when based on the information from a single breathing cycle. This can potentially decrease the need for real-time motion management techniques that prolong treatment times and decrease patient comfort.

When treating moving tumors, the precise delivery of proton therapy by pencil beam scanning (PBS) is challenged by the interplay effect. Although several 4D-optimization methods have been proposed, what is the most beneficial motion management technique is still an open question.

In this study, we wish to investigate the dosimetric impact of re-optimizing the PBS spot weights during the treatment delivery in response to, and in anticipation of, variations in the patient's breathing pattern.

We simulate for PBS the implementation of a real-time adaptive framework based on principles from receding horizon control. We consider the patient motion as characterized by a one-dimensional amplitude signal and a 4DCT, to simulate breathing of variable frequency. The framework tracks the signal and predicts the future motion with uncertainty increasing with the length of the prediction horizon. After each delivered energy layer, the framework re-optimizes the spot weights of the next layer based on the delivered dose and the predicted motion. For three lung patients, we generate 500 variable breathing patterns to evaluate the dosimetric results of the framework and compare them to those of implementations of previously proposed non-adaptive methods.

Compared to the best non-adaptive method, the adaptive framework improves the CTV D98 in the near-worst breathing scenario (5th percentile), from 96.4 to 98.9 % of the prescribed dose and considerably reduces the variation as measured by a mean decrease in the inter-quartile range by more than 80 %. The target coverage improvements are achieved without generally compromising target dose homogeneity or OAR dose. The study indicates that a motion-adaptive approach based on re-optimization of spot weights during delivery has the potential to substantially improve the dosimetric performance of PBS given fast and accurate models of patient motion. 

The interplay between the beam delivery time structure and the patient motion makes 4D dose calculation (4DDC) important when treating moving tumors with intensity modulated proton therapy. 4DDC based on phase sorting of a 4DCT suffers from approximation errors in the assignment of spots to phases, since the temporal image resolution of the 4DCT is much lower than that of the delivery time structure. In this study we investigate and address this limitation by a method which applies registration-based interpolation between phase images to increase the temporal resolution of the 4DCT. First, each phase image is deformed toward its neighbor using the deformation vector field that aligns them, scaled by the desired time step. Then Monte Carlo-based 4DDC is performed on both the original 4DCT (10 phases), and extended 4DCTs at increasingly fine temporal resolutions. The method was evaluated on seven lung cancer patients treated with three robustly optimized beams, with simulated delivery time structures. Errors resulting from limited temporal resolution were measured by comparisons of doses computed using extended 4DCTs of various resolutions. The dose differences were quantified by gamma pass rates and volumes of the CTV that had dose differences above certain thresholds. The ground truth was taken as the dose computed using 100 phase images, and was justified by considering the diminishing effects of adding more images. The effect on dose-averaged linear energy transfer was also included in the analysis. A resolution of 20 (30) phase images per breathing cycle was sufficient to bring mean CTV γ-pass rates for 3%/3mm (2%/2mm) above 99%. For the patients with well behaved image data, mean CTV γ-pass rates for 1%/1mm surpassed 99% at a resolution of 50 images. 

Robust optimization is a commonly employed method to mitigate uncertainties in the planning of intensity-modulated proton therapy (IMPT). In certain contexts, the large number of uncertainty scenarios makes the robust problem impractically expensive to solve. Recent developments in research on IMPT treatment planning indicate that the number of ideally considered error scenarios may continue to increase.In this paper, we therefore investigate methods that reduce the size of the scenario set considered during the robust optimization. Six cases of patients with non-small cell lung cancer are considered. First, we investigate the existence of an optimal subset of scenarios that needs to be considered during robust optimization, and perform experiments to see if the set can be found in a reasonable time and substitute for the full set of scenarios during robust IMPT optimization. We then consider heuristic methods to estimate this subset or find subsets with similar properties. Specifically, we select a subset of maximal diversity in terms of scenario-specific features such as the dose distributions and function gradients at the initial point. Finally, we consider adversarial methods as an alternative to solving the full robust problem and investigate the impact on computation times.The results indicate that the optimal subset can be well approximated by solving the robust IMPT problem with conventional methods. Of the methods designed to approximate it within a practically useful time frame, the results of the diversity-maximization methods indicate that they may perform better than a manual selection of scenarios based on the patient geometry. In addition, the adversarial approaches decreased the computation time by at least half compared to the conventional approach.