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- W2067656024 abstract "Purpose: To demonstrate the importance of optimizing compensator parameters in passive scattering proton therapy (PSPT). Method:We examined the plans of four patients treated for lung tumors at MD Anderson Cancer Center. Comparable IMRT and PSPT plans had been created for each of these patients. The clinical PSPT plans were calculated using 5 mm drill bit compensators with smearing values of 0.5 to 0.9 cm. Two new plans were created for each patient: one with a smearing value of 0 cm and a second with 0 cm smearing and smooth compensators. The prescribed isodose line of each plan was changed so that PTV D95 was equal in a given patient's plans and robustness analysis was performed to evaluate the impact of the compensator changes. A plan was considered robust if ITV D95 remained greater than 95% of the prescribed dose in the worst case scenario for target coverage. The robust plan with altered compensator parameters and most improved normal tissue sparing was considered to be the compensator optimized plan. Results: Mean lung dose for the 4 patients is 19.8, 12.7, 11.9 and 12.4 Gy in the compensator optimized plans compared to 21.8, 14.2, 12.9 and 13.6 Gy in the clinical plans; esophagus V60 is 39.2%, 13.4%, 7.7% and 17.7% compared to 48.6%, 18.1%, 11.6% and 20.7%; mean heart dose is 23.3, 1.2, 8.2 and 4.6 Gy compared to 24.7, 2.4, 9.5 and 8.3 Gy. Conclusion: Robustness analysis allows reduction of the smearing parameter while keeping confidence that the plan is robust against range and setup uncertainties. Compensator parameters should be individually optimized if PSPT plans are to display maximum advantage over IMRT plans in terms of dose volume data." @default.
- W2067656024 created "2016-06-24" @default.
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- W2067656024 date "2013-06-01" @default.
- W2067656024 modified "2023-09-24" @default.
- W2067656024 title "MO-G-137-05: Improving Passive Scattering Proton Therapy Plan Quality by Optimizing Compensator Parameters" @default.
- W2067656024 doi "https://doi.org/10.1118/1.4815318" @default.
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