Systematic review of 17 studies shows advanced Monte Carlo strategies outperform or match conventional algorithms in clinical linear accelerators
This systematic review evaluates the clinical relevance of advanced Monte Carlo-based dose-calculation strategies compared to conventional algorithms within clinical linear accelerators. The analysis included 467 records identified, 312 screened, and 17 studies ultimately included across Varian-, Elekta-, and Siemens-associated platforms. The scope covered dose accuracy, workflow efficiency, simulation techniques, clinical application, and performance outcomes.
The review reports that Monte Carlo-based dose calculations consistently outperformed or matched conventional algorithms. Specific findings include GPU implementations achieving 50--2500x speed improvements with less than 1% reported dose deviation. AI applications were primarily used to reduce noise and computation time. The Elekta Monaco TPS MC engine was described as a clinically validated fast MC engine, while Varian-associated workflows more commonly use MC for independent quality assurance. Studies on Elekta Unity MR-Linac confirmed accurate modeling of magnetic-field effects, and only one included study addressed the Siemens LINAC platform.
The authors highlight a key limitation: direct evidence linking dosimetric gains to improved clinical outcomes remains limited. Consequently, while accelerated MC strategies now permit accurate and efficient dose calculation that may support routine clinical workflows, the certainty of translating these technical improvements into patient benefit is not fully established. No adverse events or discontinuations were reported in the included literature.
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