Optimizing Radiation Therapy

Hybrid Proton-Photon Inverse Optimization to Deliver Radiation Therapy

Application

Hybrid proton-photon inverse optimization method that synergizes proton and photon therapy with improved total optimization objectives for robust cancer treatment delivery.

Key Benefits

Improved planning target volume coverage and organ at risk sparing.
Reduced overall optimization values.
Less expensive treatment than proton therapy alone.

Market Summary

Of all cancer treatment methods, radiation therapy (RT) is one of the more cost-effective cancer treatment modalities throughout the world. Approximately 50 percent of all cancer patients can benefit from RT in the management of their disease. The goal of RT is to deliver tumoricidal dose to clinical target volume (CTV) while sparing organs-at-risk (OAR). However, the latest methods of RT using protons or photons have important limitations. In proton therapy, the dosimetric uniformity and coverage of CTV 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 planning target volume (PTV), although it sometimes can be more difficult to spare OAR. Therefore, a robust radiation treatment delivery method is yet to be developed that can reduce the risk for adjacent organs and also covers the entire tumor.

Technical Summary

Emory researchers have developed a hybrid proton-photon inverse optimization method that models the total dose as the sum of proton and photon dose, and simultaneously optimizes proton and photon variables for robust treatment delivery. For all cases, hybrid proton-photon optimization improved target coverage and optimization objective from robust proton optimization, and was more robust to treatment uncertainty. For prostate cases, hybrid proton-photon optimization also improved sparing of organs-at-risk from robust proton optimization. This efficient inverse optimization method reduces overall optimization objective values and potentially improves the plan quality from proton- and photon-only plans via robust proton optimization.