Biological optimization for hybrid proton-photon radiotherapy.

OBJECTIVE: Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OAR), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OAR, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose. Approach: The proposed hybrid biological dose optimization method optimizes proton and photon plan variables, along with the number of fractions, minimizing biological dose to OAR and surrounding normal tissues. Hybrid plans are designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for proton and photon fraction doses. Probabilistic formulation is utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit (MMU) constraint and dose-volume histogram (DVH) constraints, is solved using an iterative convex relaxation method. Main results: Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies. Significance: This study presents a novel hybrid biological treatment planning method capable of generating plans with minimized biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly. .