Advanced dose calculation strategies for clinical linear accelerators: a systematic review

Delivering accurate radiation doses in heterogeneous tissues is critical in radiotherapy, yet conventional algorithms often lose accuracy in complex scenarios. Monte Carlo (MC) simulation offers a high-fidelity approach, but routine clinical use has historically been limited by computation time.

This systematic review evaluates the clinical relevance of advanced MC-based dose-calculation strategies for linear accelerators (LINACs), with emphasis on developments from 2010 to 2025 that improve dose accuracy and workflow efficiency, particularly through GPU acceleration and artificial intelligence (AI).

Following the PRISMA 2020 guideline, PubMed, Scopus, and Web of Science were searched. The search identified 467 records; after deduplication, 312 were screened, and 17 eligible studies were included across Varian-, Elekta-, and Siemens-associated platforms. Data were extracted on simulation techniques, clinical application, and performance outcomes. Study quality was appraised using a predefined four-domain framework informed by the AAPM TG-268 RECORDS checklist, assessing methodological rigor, validation completeness, clinical relevance, and uncertainty analysis. Data synthesis followed the Synthesis Without Meta-analysis (SWiM) guidance.

MC-based dose calculations consistently outperformed or matched conventional algorithms in small-field, heterogeneity-rich, and magnetic-field scenarios. GPU implementations achieved 50--2500x speed improvements with less than 1% reported dose deviation. AI applications were used mainly to reduce noise and computation time. Elekta's Monaco TPS includes a clinically validated fast MC engine, whereas Varian-associated workflows more commonly use MC for independent quality assurance. Studies involving the Elekta Unity MR-Linac confirmed accurate modeling of magnetic-field effects. Only one included study addressed a Siemens LINAC platform.

Accelerated MC strategies now permit accurate and efficient dose calculation that may support routine clinical workflows. However, direct evidence linking these dosimetric gains to improved clinical outcomes remains limited.

https://osf.io/ftnbs, identifier 10.17605/OSF.IO/FTNBS.