{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:08:33Z","timestamp":1787069313398,"version":"build-2736575974"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/osf.io\/ftnbs<\/jats:ext-link>\n                      , identifier 10.17605\/OSF.IO\/FTNBS.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1790425","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:20:10Z","timestamp":1777609210000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advanced dose calculation strategies for clinical linear accelerators: a systematic review"],"prefix":"10.3389","volume":"16","author":[{"given":"Ali H. 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