Clinical implementation in proton therapy of multi-field optimization by a hybrid method combining conventional PTV...

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Robust optimization is crucial in proton therapy to account for uncertainties.
• Monte Carlo (MC) algorithms offer high accuracy but are computationally intensive for robust optimization.
• Existing multi-field optimization (MFO) techniques can be complex and time-consuming.

Purpose of the Study:

• To implement and evaluate a novel hybrid multi-field optimization (hMFO) technique for robust proton therapy planning.
• To assess the compatibility of hMFO with Monte Carlo (MC) algorithms.
• To compare the robustness and plan quality of hMFO against single-field optimization (SFO) and full robust multi-field optimization (fMFO).

Main Methods:

• A hybrid robust multi-field optimization (hMFO) approach was developed, incorporating setup errors into the planning target volume (PTV) and range uncertainties into the PTV robust optimization.
• Proton therapy plans were generated for nine patients using hMFO, SFO, and fMFO.
• Plans were compared based on organ at risk (OAR) sparing and robustness to physical uncertainties and variable relative biological effectiveness (RBE).

Main Results:

• hMFO significantly reduced the number of scenarios for robust optimization compared to fMFO (3 vs. 21), enabling MC-based application.
• Normalized hMFO plans showed superior OAR sparing compared to SFO (p < 0.01) with no significant difference from fMFO.
• hMFO demonstrated robustness comparable to fMFO in worst-case scenarios and was unaffected by variable RBE.

Conclusions:

• hMFO provides a clinically feasible approach for robust optimization in proton therapy using MC algorithms.
• The technique improves plan quality over SFO without compromising robustness to setup, range, or RBE uncertainties.
• hMFO offers a practical balance between computational efficiency and plan robustness.