Room: Stars at Night Ballroom 1
Purpose: We have developed a fast analytical dose-averaged linear-energy-transfer (LETd) calculation algorithm for intensity-modulated proton therapy (IMPT) based on the traditional 1D analytical model, which works well in the high-dose regions. However, the calculated LETd in the penumbra regions deviates from the more accurate but time-consuming Monte-Carlo (MC) simulations (Fig.1). Hence, we developed a fast and accurate hybrid 3D analytical LETd calculation approach in IMPT.
Methods: The method is analogous to the pencil beam algorithm to calculate the dose in IMPT with the 3D LETd calculation kernel generated by MC as follows, step-1: Using a well-benchmarked MC code to generate LETd distributions of single energy proton beams in water for 97 clinical energies and derive the LETd lateral profiles at various depths, step-2: Using a customized â€œerror functionâ€? to fit the aforementioned LETd lateral profiles (Fig.1), and store the fitted coefficients as a lookup table, step-3: During the dose/LETd calculation, the stored coefficients and the fitting function will be used to calculate the LETd based on the spot energies and the water equivalence thickness in both beam and lateral directions. We then validated the improvement of our new hybrid method by comparing the calculated LETd distributions with MC simulations in twelve patients with different disease sites.
Results: The passing rate (averageÂ±standard deviation) of 3D Gamma analysis (3%/2mm) comparing LETd distributions calculated by the hybrid method and the 1D method to MC simulations is improved from 94.0(Â±1.0)% to 98.0(Â±2.5)% (Table.1). The relative difference of LETd values between analytical calculations and MC simulations is reduced from 20-50% to 3% in the regions with 5-20% of the prescription dose for a prostate case (Fig.2).
Conclusion: Our hybrid 3D analytical method can calculate LETd in IMPT accurately and efficiently. This code has been routinely used at our center for biological effects evaluation in IMPT.
Funding Support, Disclosures, and Conflict of Interest: This research was supported by Arizona Biomedical Research Commission Investigator Award, the National Cancer Institute (NCI) Career Developmental Award, the Fraternal Order of Eagles Cancer Research Fund Career Development Award, the Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and the Kemper Marley Foundation.