Comparison of in vitro cell survival predictions using Monte Carlo methods for proton irradiation.

Phys Med

Instituto de Fisica, Pontificia Universidad Catolica de Chile, Santiago, Chile. Electronic address:

Published: January 2025

AI Article Synopsis

  • The study explores how combining theoretical models with Monte Carlo simulations can improve predictions of cell survival following radiation-induced DNA damage.
  • Methods were optimized for proton irradiation on the Chinese hamster V79 cell line, and discrepancies from existing survival data were analyzed using two different simulation approaches.
  • The findings highlight the importance of accurate modeling, revealing a new method that reduces prediction error significantly, while also refining a user-friendly interface for easier comparison of predictive models in future research.

Article Abstract

Purpose: It is possible to combine theoretical models with Monte Carlo simulations to investigate the relationship between radiation-induced initial DNA damage and cell survival. Several combinations of models have been proposed in recent years, sparking interest in comparing their predictions in view of future clinical applications.

Methods: Two in silico methods for calculating cell survival fractions were optimized for proton irradiation of the Chinese hamster V79 cell line, for LET values ranging from 3.40 and 100 keV/μm. These methods, based on different Monte Carlo codes and theoretical models, were benchmarked against published V79 cell survival data to identify the sources of discrepancies.

Results: The predictive capacities of the methods were evaluated for several proton LET values using an external dataset. After recalibrating model parameters, multiple methods were assessed. This approach helped identify sources of variation, the main one being the simulated number of DSBs, which differed by a factor up to 3 between the two Monte Carlo codes. In this process a new method was defined, that, in all but one case, allows for a reduction in prediction error of up to 56%. Additionally, a freely available GUI for computing cell survival was refined, to facilitate further comparison of diverse theoretical models.

Conclusion: The systematic comparison of two predictive chains, characterized by distinct applicability ranges and features, was conducted. Optimization and analysis of various combinations were undertaken to elucidate differences. Addressing and minimizing such discrepancies will be crucial for further enhancing the reliability of predictive models of cell survival, aiming for biologically informed treatment planning.

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Source
http://dx.doi.org/10.1016/j.ejmp.2024.104867DOI Listing

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