(Tuesday, 7/31/2018) 10:00 AM - 10:30 AM
Room: Exhibit Hall | Forum 7
Purpose: To evaluate the effects of approximations in analytical dose calculations algorithms used in current clinical Treatment Planning Systems (TPS) on treatment outcomes in lung patients treated with Protons.
Methods: We analyzed 17 patients from the proton arm of a randomized trial comparing overall survival after photon versus proton chemoradiotherapy. The cohort consisted of 7 patients treated with passively scattered proton therapy (PSPT), 7 patients treated with intensity modulated proton therapy (IMPT), and 3 patients treated with combination of PSPT and IMPT. Dose distributions computed with the clinical TPS were compared to dose distributions computed with more accurate Monte Carlo methods and the difference in target coverage between them was correlated with the incidence of local recurrence (LR).
Results: We found that TPS consistently overestimated the target coverage with an average deficit of 8%. Out of 7 PSPT patients 4 had LR with mean target coverage deficit (TCD) of 19.5% compared to 3 patients without LR with mean TCD of 10%. Out of 7 IMPT patients 2 had LR with mean TCD of 2.4% compared to 2.1% of patients without LR.
Conclusion: Our results indicate that analytical dose calculation algorithms used in current treatment planning systems underestimate the scattering in the beam line and in the heterogeneous thorax geometry, which results in underdosing of the target and is correlated to the incidence of LR. The effect is much stronger in PSPT due to additional complexity of the beam line and the patient-specific beam modifying devices. Use of more accurate MC methods is recommended for PSPT dose calculation in the lung.
Funding Support, Disclosures, and Conflict of Interest: Funding for this work was provided by National Institutes of Health grant 5U19CA021239 and Cancer Prevention and Research Institute of Texas grant RP106232.
Keywords
Taxonomy
TH- External Beam- Particle therapy: Proton therapy - computational dosimetry-Monte Carlo
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