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The comparison of collapsed cone and Monte Carlo algorithms in tangential breast planning

Published online by Cambridge University Press:  20 April 2023

Matthew Goss*
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Colin Champ
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Mark Trombetta
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Parisa Shamsesfandabadi
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Valerie DeMartino
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Rodney Wegner
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Sushil Beriwal
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
Veronica Eisen
Affiliation:
Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Pittsburgh, PA, USA
*
Author for correspondence: Matthew Goss, Allegheny Health Network Cancer Institute, Division of Radiation Oncology, Senior Clinical Physicist, 320 E. North Ave, Pittsburgh, PA 15212, USA. Tel: 412-359-6012. E-mail: Matthew.Goss@AHN.org

Abstract

Introduction:

This study compared dose metrics between tangent breast plans calculated with the historical standard collapsed cone (CC) and the more accurate Monte Carlo (MC) algorithms. The intention was to correlate current plan quality metrics from the currently used CC algorithm with doses calculated using the more accurate MC algorithm.

Methods:

Thirteen clinically treated patients, whose plans had been calculated using the CC algorithm, were identified. These plans were copied and recalculated using the MC algorithm. Various dose metrics were compared for targets and the time necessary to perform each calculation. Special consideration was given to V105%, as this is increasingly being used as a predictor of skin toxicity and plan quality. Finally, both the CC and MC plans for 4 of the patients were delivered onto a dose measurement phantom used to analyse quality assurance (QA) pass rates. These pass rates, using various evaluation criteria, were also compared.

Results:

Metrics such as the PTVeval D95% and V95% showed a variation of 6% or less between the CC and MC plans, while the PTVeval V100% showed variation up to 20%. The PTVeval V105% showed a relative increase of up to 593% after being recalculated with MC. The time necessary to perform calculations was 76% longer on average for CC plans than for those recalculated using MC. On average, the QA pass rates using 2%2mm and 3%3mm gamma criteria for CC plans were lower (19·2% and 5·5%, respectively) than those recalculated using MC.

Conclusion:

Our study demonstrates MC-calculated PTVeval V105% values are significantly higher than those calculated using CC. PTVeval V105% is often used as a benchmark for acceptable plan quality and a predictor of acute toxicity. We have also shown that calculation times for MC are comparable to those for CC. Therefore, what is considered acceptable PTVeval V105% criteria should be redefined based on more accurate MC calculations.

Type
Original Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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