SU-E-T-623: Utilizing a Hybrid Optimizer to Improve Dose Conformity during IMRT Planning

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Intensity-modulated radiation therapy (IMRT) is a cornerstone of modern cancer treatment.
• Current IMRT planning systems often employ index-dose algorithms for speed and quasi-Newton methods for segment weight optimization.
• Integrating these methods presents an opportunity to enhance treatment plan quality.

Purpose of the Study:

• To investigate the efficacy of combining index-dose and quasi-Newton optimization methods for IMRT.
• To improve normal tissue sparing in IMRT treatment planning.
• To maintain or improve target dose coverage while enhancing plan quality.

Main Methods:

• An in-house IMRT treatment planning system was utilized.
• The workflow involved optimizing fluence, generating leaf sequences and segment weights, and refining segment shapes and weights.
• Index-dose optimization was used for initial fluence and shape tuning, followed by quasi-Newton gradient search for segment weight optimization, with alternating refinement.

Main Results:

• The combined approach achieved equivalent tumor dose coverage compared to standard methods.
• For prostate cancer patients, significant reductions in rectal dose were observed (6% for V60%, 2% for V10cc).
• In head and neck cancer cases, improved sparing of organs at risk, including the spinal cord, left parotid, and larynx, was noted.

Conclusions:

• The integration of index-dose and quasi-Newton methods offers a robust strategy for enhancing IMRT plan quality.
• This combined optimization technique effectively improves normal tissue sparing without compromising target dose coverage.
• The findings highlight the potential of hybrid optimization approaches in advancing radiation therapy planning.