Robust maximization of tumor control probability for radicality constrained radiotherapy dose painting by numbers of head and neck cancer

BACKGROUND AND PURPOSE: Radiotherapy with dose painting by numbers (DPBN) needs another approach than conventional margins to ensure a geometrically robust dose coverage for the tumor. This study presents a method to optimize DPBN plans that as opposed to achieve a robust dose distribution instead robustly maximize the tumor control probability (TCP) for patients diagnosed with head and neck cancer. MATERIAL AND METHODS: Volumetric-modulated arc therapy (VMAT) plans were optimized with a robust TCP maximizing objective for different dose constraints to the primary clinical target volume (CTVT) for a set of 20 patients. These plans were optimized with minimax optimization together with dose-responses driven by standardized uptake values (SUV) from (18)F-fluorodeoxyglucose positron emission tomography ((18)FDG-PET). The robustness in TCP was evaluated through sampling treatment scenarios with isocenter displacements. RESULTS: The average increase in TCP with DPBN compared to a homogeneous dose treatment ranged between 3 and 20 percentage points (p.p.) which depended on the different dose constraints for the CTVT. The median deviation in TCP increase was below 1p.p. for all sampled treatment scenarios versus the nominal plans. The standard deviation of SUV multiplied by the CTVT volume were found to correlate with the TCP gain with R(2) ≥ 0.9. CONCLUSIONS: Minimax optimization of DPBN plans yield, based on the presented TCP modelling, a robust increase of the TCP compared to homogeneous dose treatments for head and neck cancers. The greatest TCP gains were found for patients with large and SUV heterogeneous tumors, which may give guidance for patient selection in prospective trials.