Fully-Automated Hybrid Proton-Photon Therapy Via Fraction Optimization
Presentations
TU-D1030-IePD-F5-5 (Tuesday, 7/12/2022) 10:30 AM - 11:00 AM [Eastern Time (GMT-4)]
Exhibit Hall | Forum 5
Purpose: Hybrid proton-photon therapy utilizes complementary nature of protons (for OAR sparing) and photons (for target robustness), and can provide better plan quality than proton/photon-only therapy. Currently hybrid plans have to be delivered using either proton or photon machine. This requires the determination of optimal proton and photon fraction ratios, which is time-consuming through exhaustive search (ES). This work will develop an optimization method for optimizing proton and photon fraction ratios to automate hybrid treatment planning with optimal combination of proton and photon fractions.
Methods: The new method optimizes proton and photon fraction ratios as well as proton and photon plan variables. In the new method, the total dose distribution (sum of proton dose and photon dose) is optimized for robust target coverage and optimal OAR sparing, by jointly optimizing proton spots and photon fluences, while the target dose uniformity is also enforced individually on both proton dose and photon dose, so that the hybrid plans can be separately and robustly delivered on proton or photon machine. To ensure the target dose uniformity for proton and photon plans, proton and photon fraction ratios are explicitly modeled and optimized, so that the target dose for proton and photon dose components is constrained to be a constant fraction of the total prescription dose while the plan quality based on total dose is optimized.
Results: The new method was validated in benchmark with ES. Moreover, for all cases, hybrid plans provided better overall plan quality and OAR sparing than proton/photon-only plans, better target dose uniformity and robustness than proton-only plan; for HN and brain cases, hybrid plans also had better target coverage than photon-only plans.
Conclusion: We have developed a new fraction optimization method to automate hybrid proton-photon therapy with optimal combination of proton and photon fractions.
Funding Support, Disclosures, and Conflict of Interest: This research is partially supported by the NIH Grant No. R37CA250921 and a KUCC physicist-scientist recruiting grant.
Keywords
Treatment Techniques, Inverse Planning, Treatment Planning
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