Evaluation of Lung Dose in Esophageal Cancer Radiotherapy Using Monte Carlo Simulation
Background and purpose: Radiation therapy make an important contribution in the control and treatment of cancers. Lungs are the main organs at risk in esophageal cancer radiotherapy. Difference between the dose distribution due to the treatment planning system (TPS) and the patient's body dose...
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Veröffentlicht in: | Majallah-i dānishgāh-i ulū m-i pizishkī Māzandarān 2020-02, Vol.29 (182), p.41-49 |
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Sprache: | eng |
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Zusammenfassung: | Background and purpose: Radiation therapy make an important contribution in the control and treatment of cancers. Lungs are the main organs at risk in esophageal cancer radiotherapy. Difference between the dose distribution due to the treatment planning system (TPS) and the patient's body dose is dependent on the calculation of the treatment planning system algorithm, which is more pronounced in heterogeneities such as the lung. In this study, the dose distribution of treatment planning system was compared with Monte Carlo calculations in both homogeneous and heterogeneous tissues. Materials and methods: Three dimensional planning composed of four fields were done on the CT images using the CorPLAN TPS of a SIEMENS PRIMUS linac. EGSnrc Monte Carlo simulation code was used for the same conditions. The dose distributions obtained from Monte Carlo simulation and the TPS were compared using PDD curve and Dose Difference Percentage index obtained from the two modes. Results: According to the findings, the error rate from the TPS was less than 3% in the homogeneous tissue, whereas the error in the heterogeneous tissue was higher than the standard value (more than 5%). Conclusion: The accuracy of CorPLAN TPS at homogeneous tissue is more than that in the heterogeneous tissue and this should be considered in the clinic. This study suggests that the Monte Carlo code can be used to simulate and estimate the dose distribution in radiotherapy, and in cases where the practical measurement of some dosimetric parameters is impossible or difficult, this code can be used for prediction and optimization of treatment plans. |
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ISSN: | 1735-9260 1735-9279 |