Advanced dose calculation strategies for clinical linear accelerators: a systematic review
Abstract
Background: 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.
Purpose: 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).
Methods: 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.
Results: 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…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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