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

2020 ◽  
Author(s):  
Terman Frometa-Castillo ◽  
Anil Pyakuryal ◽  
Amadeo Wals-Zurita ◽  
Asghar Mesbahi
Keyword(s):  
Effective Dose ◽  
Biological Effects ◽  
Tumor Control ◽  
Biologically Effective Dose ◽  
Tumor Control Probability ◽  
Linear Quadratic ◽  
Cell Repair ◽  
A Cell ◽  
Biological Dose ◽  
S Model

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).

Computational simulator that calculates tumor control probability in a tumor heterogeneously irradiated for fractionated radiation oncology treatments

SIMULATION ◽  
2021 ◽  
pp. 003754972110394
Author(s):  
Terman Frometa-Castillo ◽  
Anil Pyakuryal ◽  
Ganesh Narayanasamy ◽  
Amadeo Wals-Zurita ◽  
Raul Piseaux-Aillon ◽  
...  
Keyword(s):  
Ionizing Radiation ◽  
Biological Effects ◽  
Experimental Investigations ◽  
Tumor Control ◽  
Tumor Control Probability ◽  
Linear Quadratic ◽  
Analytical Formulas ◽  
Radiation Treatments ◽  
D Model ◽  
Volume Cell

Although in the ionizing radiation field many concepts and processes are currently recognized as radiobiological, there are also probabilistic ones, and a probabilistic treatment makes a better understanding about them. The purpose of this study is to develop a new radiobiological simulator that calculates the tumor control probability (TCP) for a tumor heterogeneously irradiated from a fractioned treatment. The three possible types of cells and the results of interactions of ionizing radiation with each cell of a determined volume are analyzed. For an irradiated region with a dose per fraction d, the simulator determines the radiation biological effects using the cell kill ( K) and cell sub-lethal damage, volume, cell density, cell repair of damaged cells during the interfractions, and number of fractions. K is determined from its probabilistic complement, the cell survival ( S), described with the linear-quadratic (LQ) S(d) model as K = 1 − LQ S( d). TCP is calculated from computational simulations as in the ratio of simulations with K = 100% and their total. This application opens new avenues for theoretical and experimental investigations concerning simulations of radiation treatments, and methodologies for therapy optimizations. Our simulator represents a novel methodology as TCP is calculated without analytical formulas, but based on its own probabilistic definition.

A treatment planning system with new paradigms in the effectiveness and side-effect evaluation sections

2021 ◽  
Author(s):  
Terman Frometa-Castillo ◽  
Anil Pyakuryal ◽  
Ganesh Narayanasamy ◽  
Asghar Mesbahi
Keyword(s):  
Treatment Planning ◽  
Side Effect ◽  
Normal Tissue ◽  
External Beam Radiotherapy ◽  
Planning System ◽  
Treatment Planning System ◽  
Tumor Control ◽  
Tumor Control Probability ◽  
Computational Simulations ◽  
Linear Quadratic

Abstract Aim Academic dissemination of the “SMp treatment planning system (TPS)” for external beam radiotherapy, which has been developed as a software function that could meet the definition of a device with an entirely new intended use. This system will have new paradigms in the effectiveness and side-effect (S-E) evaluation sections, where tumor control probability (TCP) is calculated with computational simulations instead of current analytical TCP models; and S-E is evaluated with the normal tissue non-complication probability (NTCP0) methodology instead of standard NTCP one. Methods Use of probabilistic foundations in the NTCP0 methodologies; and computational simulations of the interactions of ionizing radiation with the tumor tissues in the radiation oncology treatments for the TCP calculations. Results The "TCPsim" and “NTCP0cal” calculation modules of the SMp TPS, which calculate respectively TCP and NTCP0. Conclusions While the "NTCP0cal" application has unquestionable probabilistic foundations associated to normal tissue complications as a stochastic process with more than one outcome; the "TCPsim" is based on proper approaches that are result of the computational simulations that follow logic-probabilistic procedures, and probabilistic aspects, like the relationship between TCP and linear-quadratic cell survival model for a fraction with dose d.

scholarly journals Radiobiological comparison of single and dual-isotope prostate seed implants

2012 ◽  
Vol 12 (2) ◽  
pp. 154-162
Author(s):  
Courtney Knaup ◽  
Panayiotis Mavroidis ◽  
Carlos Esquivel ◽  
Sotirios Stathakis ◽  
Gregory Swanson ◽  
...  
Keyword(s):  
Quadratic Model ◽  
Tumor Control ◽  
Prostate Brachytherapy ◽  
Tumor Control Probability ◽  
Linear Quadratic ◽  
Dual Isotope ◽  
Linear Quadratic Model ◽  
Treatment Plans ◽  
Initial Seed ◽  
Iodine 125

AbstractPurpose: Several isotopes are available for low dose-rate prostate brachytherapy. Currently most implants use a single isotope. However, the use of dual-isotope implants may yield an advantageous combination of characteristics such as half-life and relative biological effectiveness. However, the use of dual-isotope implants complicates treatment planning and quality assurance. Do the benefits of dual-isotope implants outweigh the added difficulty? The goal of this work was to use a linear-quadratic model to compare single and dual-isotope implants.Materials & Methods: Ten patients were evaluated. For each patient, six treatment plans were created with single or dual-isotope combinations of 125I, 103Pd and 131Cs. For each plan the prostate, urethra, rectum and bladder were contoured by a physician. The biologically effective dose was used to determine the tumor control probability and normal tissue complication probabilities for each plan. Each plan was evaluated using favorable, intermediate and unfavorable radiobiological parameters. The results of the radiobiological analysis were used to compare the single and dual-isotope treatment plans.Results: Iodine-125 only implants were seen to be most affected by changes in tumor parameters. Significant differences in organ response probabilities were seen at common dose levels. However, after adjusting the initial seed strength the differences between isotope combinations were minimal.Conclusions: The objective of this work was to perform a radiobiologically based comparison of single and dual-isotope prostate seed implant plans. For all isotope combinations, the plans were improved by varying the initial seed strength. For the optimized treatment plans, no substantial differences in predicted treatment outcomes were seen among the different isotope combinations.

Biologically effective dose distribution based on the linear quadratic model and its clinical relevance

1995 ◽  
Vol 33 (2) ◽  
pp. 375-389 ◽  
Author(s):  
Steve P. Lee ◽  
Min Y. Leu ◽  
James B. Smathers ◽  
William H. McBride ◽  
Robert G. Parker ◽  
...  
Keyword(s):  
Effective Dose ◽  
Dose Distribution ◽  
Clinical Relevance ◽  
Quadratic Model ◽  
Biologically Effective Dose ◽  
Linear Quadratic ◽  
Linear Quadratic Model
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