A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning...

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Online adaptive proton therapy requires rapid treatment plan re-optimization to account for anatomical changes.
• Existing methods face challenges with planning constraint infeasibility, prolonging re-optimization times.
• Automated strategies are needed to improve efficiency and quality in adaptive radiation therapy.

Purpose of the Study:

• To propose a highly automated treatment plan re-optimization strategy for online adaptive proton therapy.
• To develop a rapid re-optimization method that generates quality replans.
• To address the issue of planning constraint infeasibility efficiently.

Main Methods:

• A systematic reference point method (RPM) model was developed to minimize deviations from the initial plan in the daily objective space.
• An optimization problem was formulated to estimate and iteratively relax planning constraint violations based on magnitude and clinical priority.
• The strategy was tested on head and neck and breast cancer patient cases.

Main Results:

• The RPM-based strategy produced replans comparable to offline manual replans within online time constraints for head and neck and breast cancer patients.
• Average differences in target D95 and organ at risk sparing parameters were minimal between RPM and offline replans.
• The constraint relaxation solution resolved infeasibility issues in one round for all affected patients.

Conclusions:

• The proposed RPM-based re-optimization strategy is effective for online adaptive proton therapy.
• The method demonstrates robustness in handling complex cases, including those with constraint infeasibility.
• This approach facilitates efficient and high-quality adaptive treatment planning.