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A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
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Advanced dose calculation strategies for clinical linear accelerators: a systematic review.

Ali H D Alshehri1,2, Abdulrahman Al Mopti1,2

  • 1Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.

Frontiers in Oncology
|May 18, 2026
PubMed
Summary

Advanced Monte Carlo (MC) simulations, accelerated by GPUs and AI, now offer accurate radiotherapy dose calculations for complex cases. While faster, linking these improvements to better patient outcomes requires further clinical research.

Keywords:
GPU accelerationLINAC dosimetryMonte Carlo simulationadaptive radiotherapyartificial intelligenceclinical linear acceleratorsdose calculationhigh-precision radiotherapy

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Computational Science

Background:

  • Accurate radiation dose delivery in heterogeneous tissues is crucial for radiotherapy.
  • Conventional algorithms struggle with accuracy in complex treatment scenarios.
  • Monte Carlo (MC) simulation provides high-fidelity dose calculation but is limited by computation time.

Purpose of the Study:

  • To systematically review advanced MC-based dose calculation strategies for linear accelerators (LINACs).
  • To evaluate developments from 2010-2025 enhancing dose accuracy and workflow efficiency.
  • To focus on the impact of GPU acceleration and artificial intelligence (AI) in MC simulations.

Main Methods:

  • Systematic literature search following PRISMA 2020 guidelines (PubMed, Scopus, Web of Science).
  • Inclusion of 17 eligible studies across Varian, Elekta, and Siemens LINAC platforms.
  • Data synthesis using Synthesis Without Meta-analysis (SWiM) guidance and quality appraisal via a predefined framework.

Main Results:

  • MC dose calculations outperformed conventional methods in challenging scenarios (small fields, heterogeneity, magnetic fields).
  • GPU acceleration yielded 50-2500x speed improvements with <1% dose deviation.
  • AI primarily reduced noise and computation time; Elekta's Monaco TPS features a validated fast MC engine.

Conclusions:

  • Accelerated MC strategies enable accurate, efficient dose calculations for clinical workflows.
  • Direct evidence linking dosimetric gains to improved clinical outcomes is currently limited.