Purpose To assess the impact of approximations in current analytical dose calculation methods (ADCs) on tumor control probability (TCP) in proton therapy. Methods Dose distributions planned with ADC were compared with delivered dose distributions as determined by Monte Carlo simulations. A total of 50 patients were investigated in this analysis with 10 patients per site for 5 treatment sites (head and neck, lung, breast, prostate, liver). Differences were evaluated using dosimetric indices based on a dose-volume histogram analysis, a γ-index analysis, and estimations of TCP. Results We found that ADC overestimated the target doses on average by 1% to 2% for all patients considered. The mean dose, D95, D50, and D02 (the dose value covering 95%, 50% and 2% of the target volume, respectively) were predicted within 5% of the delivered dose. The γ-index passing rate for target volumes was above 96% for a 3%/3 mm criterion. Differences in TCP were up to 2%, 2.5%, 6%, 6.5%, and 11% for liver and breast, prostate, head and neck, and lung patients, respectively. Differences in normal tissue complication probabilities for bladder and anterior rectum of prostate patients were less than 3%. Conclusion Our results indicate that current dose calculation algorithms lead to underdosage of the target by as much as 5%, resulting in differences in TCP of up to 11%. To ensure full target coverage, advanced dose calculation methods like Monte Carlo simulations may be necessary in proton therapy. Monte Carlo simulations may also be required to avoid biases resulting from systematic discrepancies in calculated dose distributions for clinical trials comparing proton therapy with conventional radiation therapy.
|Number of pages||8|
|Journal||International Journal of Radiation Oncology Biology Physics|
|Publication status||Published - 2015 Aug 1|
Bibliographical noteFunding Information:
This work was supported by NIH/NCI under CA U19 21239. We would like to thank the MGH Monte Carlo group for many fruitful discussions. We also thank Jonathan Jackson and Tao Song of the Enterprise Research Infrastructure and Services (ERIS) group at Partners Healthcare for their in-depth support and smooth computing cluster operations, upgrades, and fixes.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Cancer Research