Interplay-robust optimization for treating irregularly breathing lung patients with pencil beam scanning.

Abstract

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.