Robust optimization in 4D treatment planning for carbon ion therapy of lung tumors

Particle therapy (PT) with scanned carbon ions has been shown to improve the treatment of stage IV lung cancer patients through reduced dose exposure of critical organs. In order to maximize this effect, the application of intensity modulated particle therapy (IMPT) is needed. However, PT is particularly susceptible to internal dose gradients due to its range dependence. This challenge is exacerbated in the presence of organ motion. Both, motion and internal dose gradients, can be addressed by dedicated robust 4D optimization strategies. In addition, as IMPT needs congruent target volumes, only robust 4D optimization can incorporate field-specific range uncertainties and motion-induced range changes. Hence, a ’worst-case’ method was implemented into GSI’s in-house treatment planning system TRiP4D and adapted for different 4D optimization strategies, accounting for setup and range uncertainties. The uncertainty scenarios of robust optimization increase the required computer memory, especially when also motion states are explicitly considered, as for robust 4D ITV optimization. Several strategies to reduce problem size and to increase the computation speed were implemented and tested, such as splitting the optimization matrix by dose contribution or randomized voxel subsampling. Plan robustness was tested by performing robustness analysis, where dose distributions were calculated for a variety of uncertainty scenarios. By creating the superposition of patient setup errors with particle range changes, uncertainty scenarios beyond the ones already used in the optimization were tested. In a patient study with 8 complex lung cancer patients, it was possible to increase plan robustness in the majority of patients using robust optimization. For conventional optimization, especially the dose volume exposure of the smaller airways (SA) became a limiting factor. Using the same 4D ITV planning strategy but with robust optimization enabled the OAR constraint for the SA to be fulfilled in 98.8 % of the cases, up from 79.8 % for conventional optimization. It is to note, that this increase in robustness could mean sacrificing target coverage in some patients. Furthermore, a robust implementation of conformal 4D optimization was developed, based on a library of treatment plans for each motion phase of a 4DCT. The reduction of irradiated volume considerably improved OAR exposure, but increased the need for robust optimization even further in order to maintain robustness against deviations of the delivered dose distribution from the planned dose distribution. For a lung cancer patient with large tumor motion, the robust conformal 4D optimization method could be shown to generate treatment plans with increased robustness against range and setup errors. As a result of the increased robustness, target coverage could be increased and dose exposure to the OARs could be decreased at the same time. In conclusion, both robust optimization methods for 4D treatment planning in PT yield promising results, generating new options for robust, safe intensity modulated particle therapy and thus beneficial treatment plans for lung cancer patients