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Hybrid Proton-Photon Inverse Optimization for Improving Proton- and Photon-Only Plans with Treatment Uncertainty

H Gao1*, (1) Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA


(Tuesday, 7/16/2019) 7:30 AM - 9:30 AM

Room: Stars at Night Ballroom 1

Purpose: In proton therapy, the CTV coverage (uniformity and hot spots) is often compromised by range and setup uncertainty, even with robust optimization. In photon therapy, the CTV coverage is more robust to treatment uncertainty using PTV, although it sometimes can be more difficult to spare OAR. This work aims to synergize protons and photons by developing hybrid proton-photon inverse planning (HYBRID).

Methods: HYBRID models the total dose as the sum of proton and proton dose, and simultaneously optimizes proton and photon variables in addition to their numbers of fractions. Photon dose covers PTV that implicitly accounts for setup uncertainty, and proton and total dose cover CTV that explicitly account for range and setup uncertainty via robust optimization. During optimization, proton and photon components are individually regularized to have uniform target dose coverage, and number of their fractions is iteratively updated. The optimization problem with dose-volume objectives and variable fractions is nonconvex and solved by iterative convex relaxations with convex subproblems solved by alternating direction method of multipliers.

Results: HYBRID was validated in comparison with photon and proton plans using representative prostate, lung and head-and-neck cases. In all cases, HYBRID provided the best total optimization objectives (CTV+OAR). In all cases, HYBRID had smaller CTV objective value than proton plans, since the use of photon dose in HYBRID improved CTV coverage in the presence of treatment uncertainty. In all cases, HYBRID had smaller OAR objective value than photon plans, since the use of proton dose in HYBRID improved OAR sparing. Moreover, for the prostate case HYBRID also had smaller OAR objective value than proton plans as well.

Conclusion: The hybrid proton-photon inverse optimization is introduced to synergize proton and photon therapy with improved total optimization objectives from proton- and photon-only plans in the presence of treatment uncertainty.


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