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Chow, J.C.L. Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation. Encyclopedia. Available online: https://encyclopedia.pub/entry/59578 (accessed on 04 March 2026).
Chow JCL. Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation. Encyclopedia. Available at: https://encyclopedia.pub/entry/59578. Accessed March 04, 2026.
Chow, James C. L.. "Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation" Encyclopedia, https://encyclopedia.pub/entry/59578 (accessed March 04, 2026).
Chow, J.C.L. (2026, March 03). Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation. In Encyclopedia. https://encyclopedia.pub/entry/59578
Chow, James C. L.. "Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation." Encyclopedia. Web. 03 March, 2026.
Peer Reviewed
Hybrid Artificial Intelligence–Monte Carlo Methods for Radiotherapy Dose Calculation
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia6030055

Hybrid artificial intelligence (AI) and Monte Carlo (MC) methods for radiotherapy dose calculation refer to computational approaches that integrate machine learning models with physics-based MC simulation to achieve both fast and accurate estimation of radiation dose distributions. These methods use AI to approximate complex MC dose calculations with greatly reduced computation time, while retaining MC simulation as the standard for physical fidelity and validation. The hybrid strategy supports real-time or near-real-time dose evaluation, enables adaptive treatment workflows, and allows accurate modeling of photon and electron beams in heterogeneous patient anatomy. By combining the strengths of data-driven prediction and physics-based simulation, hybrid AI and MC methods provide a pathway toward efficient and high-precision dose calculation in modern radiotherapy.

Monte Carlo simulation radiotherapy dose calculation artificial intelligence machine learning deep learning U-Net architecture dose prediction adaptive radiotherapy image-guided radiotherapy radiation transport modeling
Monte Carlo (MC) simulation occupies a central place in the field of radiotherapy because it provides the most faithful representation of radiation transport available to modern computational science [1][2]. Since the earliest applications of computational physics in medicine, researchers have sought methods that can accurately describe how photons, electrons, protons, and heavy ions deposit energy in human tissue. MC simulation emerged as the preferred approach because it does not rely on simplified analytical approximations but instead models individual particle interactions based on well-established physical probability distributions [3][4]. Through the accumulation of many simulated particle histories, it is possible to reconstruct a three-dimensional dose distribution that closely reflects the true behavior of radiation in the body. This capability has allowed MC simulation to become a benchmark for validating new treatment planning systems [5][6], commissioning linear accelerators [7], and exploring advanced radiotherapy techniques [8].
As radiotherapy has evolved toward increasingly precise and conformal treatments, the importance of accurate dose calculation has grown accordingly. Modern treatment modalities require the dose distribution to be predicted with high fidelity in the presence of complex anatomical features, such as air cavities, bone interfaces, irregular surfaces, and internal organ motion [9][10][11][12]. Analytical dose calculation algorithms can struggle under these conditions because they often rely on assumptions that break down in heterogeneous regions. MC simulation, by contrast, retains accuracy even in challenging scenarios because it models the relevant physics directly. For this reason, MC simulation is frequently considered the reference standard for research in radiotherapy physics and is often used as the final authority when comparing or validating alternative dose calculation algorithms [13]. Figure 1 shows an overview of the major applications of MC simulation in radiotherapy. These applications include beam modeling, patient dose calculation, treatment planning system verification, imaging and dosimetry research, and advanced studies involving novel radiation delivery techniques. The diagram illustrates how MC methods contribute to different stages of the radiotherapy process and highlights their central role in ensuring accurate and reliable computation of dose.
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Figure 1. Schematic representation of key applications of MC simulation in radiotherapy. MC methods are employed for detailed modeling of linear accelerator beam generation, three-dimensional patient dose calculation in heterogeneous anatomy, validation and commissioning of treatment planning systems, imaging-based phantom construction, and research into innovative modalities such as adaptive radiotherapy and ultra-high-dose-rate (FLASH) irradiation. These applications highlight the central role of MC simulation in accurate dosimetry.
Despite these strengths, MC simulation has historically been limited by its computational demands. A typical simulation must track millions or even billions of particle histories to reduce statistical uncertainty to clinically acceptable levels. Each particle undergoes numerous potential interactions, and each of these interactions must be sampled from probability distributions derived from theoretical or experimental cross-sectional data. Carrying out this process for a large particle population requires significant computational resources and time. On a conventional computing system, a single high-resolution MC calculation may require several minutes to complete. Although shorter runtimes are possible with highly parallel architectures [14], such resources are not always accessible in routine clinical settings.
These computational constraints have become a major challenge as radiotherapy shifts toward workflows that require rapid feedback. One of the clearest examples is image-guided adaptive radiotherapy [15]. In this approach, the patient is imaged immediately before treatment, and the treatment plan is adapted to reflect the current anatomical state. Dose calculation must therefore be performed quickly [16], often within the short window of time in which the patient is positioned on the treatment couch. Classical MC simulation cannot meet this requirement because it cannot complete a full dose calculation within the necessary timeframe. As a result, the use of MC simulation in adaptive workflows has been limited to research settings and retrospective analysis rather than real-time clinical decision-making.
The increasing incorporation of artificial intelligence (AI) into radiotherapy has created new opportunities to address this limitation [17]. AI, particularly in the form of deep learning, has demonstrated the ability to learn complex spatial and physical relationships from large datasets. When trained on MC-generated dose distributions, deep learning models can reproduce many essential features of a full simulation while requiring only a fraction of the computation time. Once trained, such models can evaluate dose almost instantly, which opens the possibility of performing near-real-time dose calculations during treatment planning or adaptive workflows [18].
The combination of AI and MC simulation provides a path toward computational frameworks that are both accurate and efficient. AI can act as a surrogate for rapid dose estimation, while MC simulation remains the authoritative standard against which predictions are validated [19]. This hybrid strategy has the potential to transform radiotherapy computation by enabling real-time treatment adaptation, improving the speed of planning, and supporting the study of advanced delivery techniques. The purpose of this entry is to present an overview of these hybrid methods, their scientific foundations, and their relevance to the future of radiotherapy practice and research.

References

  1. Andreo, P. Monte Carlo simulations in radiotherapy dosimetry. Radiat. Oncol. 2018, 13, 121.
  2. Chow, J.C. Monte Carlo simulations in nanomedicine: Advancing cancer imaging and therapy. Nanomaterials 2025, 15, 117.
  3. Seco, J.; Verhaegen, F. Monte Carlo Techniques in Radiation Therapy; CRC Press: Boca Raton, FL, USA, 2013.
  4. Bielajew, A. The Monte Carlo simulation of radiation transport. In Handbook of Radiotherapy Physics: Theory and Practice; Hodder Arnold: London, UK, 2007; pp. 75–87.
  5. Brualla, L.; Rodriguez, M.; Lallena, A.M. Monte Carlo systems used for treatment planning and dose verification. Strahlenther. Onkol. 2017, 193, 243–259.
  6. Lopez-Tarjuelo, J.; Garcia-Molla, R.; Juan-Senabre, X.J.; Quirós-Higueras, J.D.; Santos-Serra, A.; de Marco-Blancas, N.; Calzada-Feliu, S. Acceptance and commissioning of a treatment planning system based on Monte Carlo calculations. Technol. Cancer Res. Treat. 2014, 13, 129–138.
  7. Can, S.; Karaçetin, D.; Meriç, N. Beam modeling and commissioning for Monte Carlo photon beam on an Elekta Versa HD LINAC. Appl. Radiat. Isot. 2022, 180, 110054.
  8. Chow, J.C.; Ruda, H.E. Impact of scattering foil composition on electron energy distribution in a clinical linear accelerator modified for FLASH radiotherapy: A Monte Carlo study. Materials 2024, 17, 3355.
  9. Reis, C.Q.; Nicolucci, P.; Fortes, S.S.; Silva, L.P. Effects of heterogeneities in dose distributions under nonreference conditions: Monte Carlo simulation vs dose calculation algorithms. Med. Dosim. 2019, 44, 74–82.
  10. Pokhrel, D.; McClinton, C.; Sood, S.; Badkul, R.; Saleh, H.; Jiang, H.; Lominska, C. Monte Carlo evaluation of tissue heterogeneities corrections in the treatment of head and neck cancer patients using stereotactic radiotherapy. J. Appl. Clin. Med. Phys. 2016, 17, 258–270.
  11. Paganetti, H.; Jiang, H.; Adams, J.A.; Chen, G.T.; Rietzel, E. Monte Carlo simulations with time-dependent geometries to investigate effects of organ motion with high temporal resolution. Int. J. Radiat. Oncol. Biol. Phys. 2004, 60, 942–950.
  12. Galeone, C.; Nakas, A.; Donetti, M.; Martire, M.C.; Milian, F.M.; Pella, A.; Paganelli, C.; Sacchi, R.; Vignati, A.; Durante, M.; et al. Real-time motion modeling and treatment verification for irregular motion in carbon ion therapy: A feasibility study. Phys. Med. Biol. 2025, 70, 165009.
  13. Kowatsch, M.; Partoll, E.; Künzler, T.; Attenberger, C.; Szeverinski, P.; Clemens, P.; Tschann, P. Quality assurance of beam base data in modern radiotherapy: A Monte Carlo simulation approach. Biomed. Phys. Eng. Express 2025, 11, 045010.
  14. Chow, J.C. A performance evaluation on Monte Carlo simulation for radiation dosimetry using cell processor. J. Comput. Methods Sci. Eng. 2011, 11, 1–12.
  15. Liu, Y.; Yang, J.; Lee, P.; Horowitz, D.; Thomas, T.O. Adaptive radiation therapy. Adv. Oncol. 2025, 5, 1–13.
  16. Chow, J.C. Quantum computing and machine learning in medical decision-making: A comprehensive review. Algorithms 2025, 18, 156.
  17. Webster, M.; Podgorsak, A.; Li, F.; Zhou, Y.; Jung, H.; Yoon, J.; Dona Lemus, O.; Zheng, D. New approaches in radiotherapy. Cancers 2025, 17, 1980.
  18. Rabe, M.; Kurz, C.; Thummerer, A.; Landry, G. Artificial intelligence for treatment delivery: Image guided radiotherapy. Strahlenther. Onkol. 2025, 201, 283–297.
  19. Chow, J.C. Nanomaterial-based molecular imaging in cancer: Advances in simulation and AI integration. Biomolecules 2025, 15, 444.
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