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Impact of proton dose calculation algorithms on the interplay effect in PBS proton based SBRT lung plans.

Suresh RanaAnatoly B Rosenfeld
Published in: Biomedical physics & engineering express (2021)
Purpose. The purpose of the current study was to investigate the impact of RayStation analytical pencil beam (APB) and Monte Carlo (MC) algorithms on the interplay effect in pencil beam scanning (PBS) proton-based stereotactic body radiation therapy (SBRT) lung plans.Methods. The currentin-silicoplanning study was designed for a total dose of 5000 cGy(RBE) with a fractional dose of 1000 cGy(RBE). First, three sets of nominal plans were generated for each patient: (a) APB optimization followed by APB dose calculation (PB-PB), (b) APB optimization followed by MC dose calculation (PB-MC), and (c) MC optimization followed by MC dose calculation (MC-MC). Second, for each patient, two sets of volumetric repainting plans (five repaintings) - PB-MCVR5and MC-MCVR5were generated based on PB-MC and MC-MC, respectively. Dosimetric differences between APB and MC algorithms were calculated on the nominal and interplay dose-volume-histograms (DVHs).Results. Interplay evaluation in non-volumetric repainting plans showed that APB algorithm overestimated the target coverage by up to 8.4% for D95%and 10.5% for D99%, whereas in volumetric repainting plans, APB algorithm overestimated by up to 5.3% for D95%and 7.0% for D99%. Interplay results for MC calculations showed a decrease in D95%and D99%by average differences of 3.5% and 4.7%, respectively, in MC-MC plans and by 1.8% and 3.0% in MC-MCVR5plans.Conclusion. In PBS proton-based SBRT lung plans, the combination of APB algorithm and interplay effect reduced the target coverage. This may result in inferior local control. The use of MC algorithm for both optimization and final dose calculations in conjunction with the volumetric repainting technique yielded superior target coverage.
Keyphrases
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  • monte carlo
  • machine learning
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