open access
Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation algorithm in volumetric modulated arc therapy using Monaco TPS for three different clinical cases
open access
Abstract
Aim
To study the dosimetric impact of statistical uncertainty (SU) per plan on Monte Carlo (MC) calculation in Monaco™ treatment planning system (TPS) during volumetric modulated arc therapy (VMAT) for three different clinical cases.
Background
During MC calculation SU is an important factor to decide dose calculation accuracy and calculation time. It is necessary to evaluate optimal acceptance of SU for quality plan with reduced calculation time.
Materials and methods
Three different clinical cases as the lung, larynx, and prostate treated using VMAT technique were chosen. Plans were generated with Monaco™ V5.11 TPS with 2% statistical uncertainty. By keeping all other parameters constant, plans were recalculated by varying SU, 0.5%, 1%, 2%, 3%, 4%, and 5%. For plan evaluation, conformity index (CI), homogeneity index (HI), dose coverage to PTV, organ at risk (OAR) dose, normal tissue receiving dose ≥5Gy and ≥10Gy, integral dose (NTID), calculation time, gamma pass rate, calculation reproducibility and energy dependency were analyzed.
Results
CI and HI improve as SU increases from 0.5% to 5%. No significant dose difference was observed in dose coverage to PTV, OAR doses, normal tissue receiving dose ≥5Gy and ≥10Gy and NTID. Increase of SU showed decrease in calculation time, gamma pass rate and increase in PTV max dose. No dose difference was seen in calculation reproducibility and dependent on energy.
Conclusion
For VMAT plans, SU can be accepted from 1% to 3% per plan with reduced calculation time without compromising plan quality and deliverability by accepting variations in point dose within the target.
Abstract
Aim
To study the dosimetric impact of statistical uncertainty (SU) per plan on Monte Carlo (MC) calculation in Monaco™ treatment planning system (TPS) during volumetric modulated arc therapy (VMAT) for three different clinical cases.
Background
During MC calculation SU is an important factor to decide dose calculation accuracy and calculation time. It is necessary to evaluate optimal acceptance of SU for quality plan with reduced calculation time.
Materials and methods
Three different clinical cases as the lung, larynx, and prostate treated using VMAT technique were chosen. Plans were generated with Monaco™ V5.11 TPS with 2% statistical uncertainty. By keeping all other parameters constant, plans were recalculated by varying SU, 0.5%, 1%, 2%, 3%, 4%, and 5%. For plan evaluation, conformity index (CI), homogeneity index (HI), dose coverage to PTV, organ at risk (OAR) dose, normal tissue receiving dose ≥5Gy and ≥10Gy, integral dose (NTID), calculation time, gamma pass rate, calculation reproducibility and energy dependency were analyzed.
Results
CI and HI improve as SU increases from 0.5% to 5%. No significant dose difference was observed in dose coverage to PTV, OAR doses, normal tissue receiving dose ≥5Gy and ≥10Gy and NTID. Increase of SU showed decrease in calculation time, gamma pass rate and increase in PTV max dose. No dose difference was seen in calculation reproducibility and dependent on energy.
Conclusion
For VMAT plans, SU can be accepted from 1% to 3% per plan with reduced calculation time without compromising plan quality and deliverability by accepting variations in point dose within the target.
Keywords
Statistical uncertainty; Monte Carlo dose algorithm; VMAT; Lung; Larynx; Prostate
About this articleTitle
Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation algorithm in volumetric modulated arc therapy using Monaco TPS for three different clinical cases
Journal
Reports of Practical Oncology and Radiotherapy
Issue
Pages
188-199
Published online
2019-03-01
DOI
10.1016/j.rpor.2019.01.005
Bibliographic record
Rep Pract Oncol Radiother 2019;24(2):188-199.
Keywords
Statistical uncertainty
Monte Carlo dose algorithm
VMAT
Lung
Larynx
Prostate
Authors
Mohandass Palanisamy
Khanna David
Manigandan Durai
Narendra Bhalla
Abhishek Puri