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Monte Carlo modeling for the computation of in-field and out-of-field doses in 3D conformal radiotherapy of breast cancer
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N. Rafat-Motavalli   , H. Miri-Hakimabad , E. Hoseinian-Azghadi , L. Rafat-Motavalli , M. Dayyani , T. Tuğrul |
| Department of Physics, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran , mirihakim@um.ac.ir |
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Abstract: (12 Views) |
Background: To generate the Monte Carlo (MC) 3D dose distribution in both in-field and out-of-field regions for three-dimensional conformal radiotherapy (3DCRT) of breast cancer patients and to compare it with the dose calculations from two available commercial treatment planning systems (TPS). Material and Methods: The Prowess Panther TPS was initially used to create the treatment plan and underwent a simulation process using the MCNPX 2.6.0 Monte Carlo code. Dose maps, profiles, dose volume histograms (DVHs), and mean doses for organs at risk (OARs) were calculated. To evaluate the accuracy of dose calculations, global gamma index analysis was conducted to ensure conformity between MC dose calculations and the two TPSs. Results: For individual beams, comparing Prowess to MC shows that the gamma passing rates (GPR) range from 94.3% to 99.2%, with a GPR of 91.2% for all beams combined. In contrast, when comparing RayStation to MC, the GPRs range from 96.0% to 99.0%, with a total beam GPR of 94.3%. Regarding out-of-field doses, the two TPSs can provide reliable dose calculations only for organs receiving mean doses greater than 500-700 cGy. Conclusion: The MC model was benchmarked for 3DCRT plans, demonstrating that it could provide accurate MC doses in the in-field area. Furthermore, the comparison between MC and TPS doses shows that RayStation performs better in the in-field region. Nonetheless, both TPS-based out-of-field estimates were unreliable, indicating the importance of utilizing MC methods to estimate out-of-field doses. |
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| Keywords: Monte Carlo, breast cancer, radiotherapy. |
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Full-Text [PDF 1429 kb]
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Type of Study: Original Research |
Subject:
Radiation Biology
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