<abstract xmlns="http://www.w3.org/1999/xhtml">

<italic>Introduction:</italic> Proton radiotherapy offers an advantage in sparing healthy tissue compared to photon therapy due to the specific interaction of protons with the patient’s body. In radiobiological experiments, alpha sources are commonly used instead of proton accelerators for convenience, but ensuring a uniform dose distribution is challenging. Properly designing the cell irradiation setup is crucial to reliably measure the average cellular response in such experiments. The objective of this research is to underscore the importance of dosimetric validation in radiobiological investigations. While Monte Carlo (MC) simulations offer valuable insights, their accuracy needs experimental confirmation. Once consistent results are obtained, the reliance on simulations becomes viable, as they are more efficient and less cumbersome compared to experimental procedures.
<italic>Material and methods:</italic> The simulations are performed with three MC code-based tools: Geant4-DNA, GATE, and SRIM to model the alpha radiation source and calculate dose distributions for various cell irradiation scenarios. Dosimetric verification of the experimental setup containing a 241Am source is performed using radiochromic films. Additionally, a clonogenic cell survival assay is carried out for the DU145 cell line.
<italic>Results:</italic> The study introduces a novel source simulation model derived from dosimetric measurements. The comparison between dosimetric results obtained with simulations and measured experimentally yields a gamma (3%/3mm) parameter value exceeding 99%. Furthermore, the LQ model parameters fitted to survival data of DU145 cells irradiated with particles emitted from 241Am source demonstrate consistency with previously published findings.
<italic>Conclusions:</italic> Radiobiological experiments investigate cellular responses to various irradiation scenarios. Challenges arise with densely ionizing radiation used in clinical practice, particularly in ensuring uniform dose delivery for reliable experiments. MC codes aid in simulating dose distribution and designing irradiation systems for consistent cell treatment. However, experimental validation is essential before relying on simulation results. Once confirmed, these results offer a cost-effective and time-efficient approach to planning radiobiological experiments compared to traditional laboratory work.
</abstract>

Introduction: Proton radiotherapy offers an advantage in sparing healthy tissue compared to photon therapy due to the specific interaction of protons with the patient’s body. In radiobiological experiments, alpha sources are commonly used instead of proton accelerators for convenience, but ensuring a uniform dose distribution is challenging. Properly designing the cell irradiation setup is crucial to reliably measure the average cellular response in such experiments. The objective of this research is to underscore the importance of dosimetric validation in radiobiological investigations. While Monte Carlo (MC) simulations offer valuable insights, their accuracy needs experimental confirmation. Once consistent results are obtained, the reliance on simulations becomes viable, as they are more efficient and less cumbersome compared to experimental procedures.
Material and methods: The simulations are performed with three MC code-based tools: Geant4-DNA, GATE, and SRIM to model the alpha radiation source and calculate dose distributions for various cell irradiation scenarios. Dosimetric verification of the experimental setup containing a 241Am source is performed using radiochromic films. Additionally, a clonogenic cell survival assay is carried out for the DU145 cell line.
Results: The study introduces a novel source simulation model derived from dosimetric measurements. The comparison between dosimetric results obtained with simulations and measured experimentally yields a gamma (3%/3mm) parameter value exceeding 99%. Furthermore, the LQ model parameters fitted to survival data of DU145 cells irradiated with particles emitted from 241Am source demonstrate consistency with previously published findings.
Conclusions: Radiobiological experiments investigate cellular responses to various irradiation scenarios. Challenges arise with densely ionizing radiation used in clinical practice, particularly in ensuring uniform dose delivery for reliable experiments. MC codes aid in simulating dose distribution and designing irradiation systems for consistent cell treatment. However, experimental validation is essential before relying on simulation results. Once confirmed, these results offer a cost-effective and time-efficient approach to planning radiobiological experiments compared to traditional laboratory work.

From radiobiology to radiotherapy: dose homogeneity in cells after alpha irradiation in measurements and Monte Carlo simulations

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Introduction: Proton radiotherapy offers an advantage in sparing healthy tissue compared to photon therapy due to the specific interaction of protons with the...

From radiobiology to radiotherapy: dose homogeneity in cells after alpha irradiation in measurements and Monte Carlo simulations

Publié en ligne: 30 juin 2024

Pages: 79 - 89

Reçu: 16 janv. 2024

Accepté: 21 avr. 2024

DOI: https://doi.org/10.2478/pjmpe-2024-0009

Mots clés
Monte Carlo simulations, cell survival, dosimetry, radiation biology, high LET radiation

© 2024 Mateusz Filipek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

From radiobiology to radiotherapy: dose homogeneity in cells after alpha irradiation in measurements and Monte Carlo simulations

Publié en ligne: 30 juin 2024

Pages: 79 - 89

Reçu: 16 janv. 2024

Accepté: 21 avr. 2024

DOI: https://doi.org/10.2478/pjmpe-2024-0009

Mots clésMonte Carlo simulations, cell survival, dosimetry, radiation biology, high LET radiation

© 2024 Mateusz Filipek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Monte Carlo simulations, cell survival, dosimetry, radiation biology, high LET radiation