Simulator of radiation biological effects in tumor in order to determinate the tumor control probability

Frometa-Castillo, Terman; Pyakuryal, Anil; Piseaux‐Aillon, Raul

doi:10.1016/j.imu.2019.100217

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computational simulations have led to the development of three radiobiological simulators that determine TCP and mean RBEFs in normal tissue for regular/interrupted treatments, as well as one that obtains similar probabilistic distributions to binomial and Poisson ones. The first application is discussed in [12]. The MatLab applications of these simulators are publicly available in the repository of [13].…”

Section: The Radiobiological Computational Simulators (Rcss)mentioning

confidence: 99%

Biologically Effective Dose (BED) or Radiation Biological Effect (RBEf)?

Frometa-Castillo¹,

Pyakuryal²,

Wals-Zurita³

et al. 2020

Recent Techniques and Applications in Ionizing Radiation Research

Self Cite

View full text Add to dashboard Cite

The current radiosensitive studies are described with linear-quadratic (LQ) cell survival (S) model for one fraction with a dose d. As result of assuming all sublethally damaged cells (SLDCs) are completely repaired during the interfractions, that is, no presence of SLDCs, the survived cells are calculated for a n-fractionated regimen with the LQ S(n,D) model. A mathematically processed subpart of LQS(n,D) is the biologically effective dose (BED) that is used for evaluating a so-called "biological dose." The interactions of ionizing radiation with a living tissue can produce partial death or sublethal damage from healthy or sublethally damaged cells. The proportions of the killed and sub-lethally damaged cells define the radiation biological effects (RBEfs). Computational simulations using RBEFs for fractionated regimens let calculating tumor control probability. While the derivation of the LQ S(n,D) considers a 100% cell repair, that is, 0% of sublethally damaged cells (SLDCs), the radiobiological simulators take into account the presence of SLDCs, as well as a cell repair <100% during the interfractions and interruption. Given "biological dose" does not exist, but RBEf, there was need for creating the BED. It is shown how some uses of BED, like the derivation of EQ2D expression, can be done directly with the LQ S(n,D).

show abstract

Section: The Radiobiological Computational Simulators (Rcss)mentioning

confidence: 99%

Biologically Effective Dose (BED) or Radiation Biological Effect (RBEf)?

Frometa-Castillo¹,

Pyakuryal²,

Wals-Zurita³

et al. 2020

Recent Techniques and Applications in Ionizing Radiation Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…These models are based on SMp function of [3]. The radiobiological simulator developed in [4] is a new and interesting way of calculating probabilities.…”

Section: Introductionmentioning

confidence: 99%

Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions

Frometa-Castillo¹,

Pyakuryal²,

Wals-Zurita³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

show abstract