Dosimetric comparison of Monte Carlo, Acuros XB, and anisotropic analytical algorithm for lung cancer plans on halcyon accelerators

Kainan Shao, Chaojun Chai, Yiwei Yang, Weijun Chen and Fenglei Du

PLOS ONE, 2025, vol. 20, issue 10, 1-24

Abstract: This study evaluates the differences in application of the RayStation Monte Carlo algorithm (RMC) compared to Acuros XB (AXB) and the Anisotropic Analytical Algorithm (AAA) in Eclipse for conventional radiotherapy planning of lung cancer using the novel ring-shaped Halcyon accelerator. A total of 63 non-small-cell lung cancer patients were retrospectively included, with a prescription dose of 60 Gy delivered in 30 fractions. Radiotherapy plans were initially designed and optimized in RayStation, then recalculated in Eclipse using AXB and AAA to assess algorithmic differences in dose distributions. Analysis of data from 63 patients, combined with simulations using square fields and cylindrical water phantoms, revealed that RMC and AXB achieved high consistency in target dose coverage and conformity. High-dose indicators, such as D2% and D0.03cc, showed close agreement between RMC and AAA, while AXB tended to slightly underestimate peak doses. For prescription dose coverage metrics like D95% and D98%, the difference between RMC and AXB was less than 1%, whereas AAA exhibited minor degradation. In organ-at-risk dose evaluations, RMC delivered higher doses compared to AXB and AAA, with AAA doses exceeding those of AXB. These findings confirm the dose consistency of RayStation and Eclipse algorithms for use with the Halcyon accelerator. The RayStation Monte Carlo algorithm (RMC) is a viable alternative to AXB, especially in lung cancer cases with high tissue heterogeneity, as target coverage and conformity discrepancies remain within 0.5%. Additionally, recalculation of RMC-optimized plans in Eclipse using AXB resulted in lower organ-at-risk doses. Therefore, recalculations performed before treatment do not compromise the clinical adequacy of volumetric dose evaluations.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0334329

DOI: 10.1371/journal.pone.0334329

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