Radiobiological Models Research Articles

A clustering tool for generating biological geometries for computational modeling in radiobiology.

Objective.To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.Approach.The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the dice similarity coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.Main results.The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA double-strand-breaks after microbeam irradiation in a segmentation image of a cell culture.Significance.This tool enables the use of realistic biological geometries in computational radiobiological studies.

3187: The applied radiobiological effectiveness model influences tumor control when treating with ions

3187: The applied radiobiological effectiveness model influences tumor control when treating with ions

Enhancing treatment precision through radiobiological modeling for evaluating complex VMAT plans in prostate and head-and-neck cancers

Enhancing treatment precision through radiobiological modeling for evaluating complex VMAT plans in prostate and head-and-neck cancers

Unlocking the Potential Role of Decellularized Biological Scaffolds as a 3D Radiobiological Model for Low- and High-LET Irradiation.

Decellularized extracellular matrix (ECM) bioscaffolds have emerged as a promising three-dimensional (3D) model, but so far there are no data concerning their use in radiobiological studies. We seeded two well-known radioresistant cell lines (HMV-II and PANC-1) in decellularized porcine liver-derived scaffolds and irradiated them with both high- (Carbon Ions) and low- (Photons) Linear Energy Transfer (LET) radiation in order to test whether a natural 3D-bioscaffold might be a useful tool for radiobiological research and to achieve an evaluation that could be as near as possible to what happens in vivo. Biological scaffolds provided a favorable 3D environment for cell proliferation and expansion. Cells did not show signs of dedifferentiation and retained their distinct phenotype coherently with their anatomopathological and clinical behaviors. The radiobiological response to high LET was higher for HMV-II and PANC-1 compared to the low LET. In particular, Carbon Ions reduced the melanogenesis in HMV-II and induced more cytopathic effects and the substantial cell deterioration of both cell lines compared to photons. In addition to offering a suitable 3D model for radiobiological research and an appropriate setting for preclinical oncological analysis, we can attest that bioscaffolds seemed cost-effective due to their ease of use, low maintenance requirements, and lack of complex technology.

The Use of Survival Dose-Rate Dependencies as Theoretical Discrimination Criteria for In-Silico Dynamic Radiobiological Models.

Cell repair dynamics are crucial in optimizing anti-cancer therapies. Various assays (eg, comet assay and γ-H2AX) assess post-radiation repair kinetics, but interpreting such data is challenging and model-based data analyses are required. However, ambiguities in parameter calibration remain an unsolved challenge. To address this, we propose combining survival dose-rate effects with computer simulations to gain knowledge about repair kinetics. After a literature review, theoretical discriminators based on common fractionation/dose-rate-related effects were defined to discard unrealistic model dynamics. The Multi-Hit Repair (MHR) model was calibrated with canine osteosarcoma Abrams cell line data to study the discriminators' efficacy in scenarios with limited survival data. Additionally, survival dose-rate-dependent data from the human SiHa cervical cancer cell line were used to illustrate the survival behavior at diverse dose-rates and the capability of the MHR to model these data. SiHa data confirmed the validity of the proposed discriminators. The discriminators filtered 99% of parameter sets, improving the calibration of Abrams cells data. Furthermore, results from both cell lines may hint universal aspects of cellular repair. Dose-rate theoretical discrimination criteria are an effective method to understand repair kinetics and improve radiobiological model calibration. Moreover, this methodology may be used to analyze diverse biological data using dynamic models in-silico.

Assessment of the Dosimetric Index from IMRT and Rapid arc Plan for Oropharyngeal Cancer with Simultaneous Integrated Boost (SIB) Technique in Combination with EUD-based NTCP and TCP Radiobiological Models.

The current research compared radiobiological and dosimetric results for simultaneous integrated boost (SIB) plans employing RapidArc and IMRT planning procedures in oropharyngeal cancer from head-and-neck cancer (HNC) patients. The indigenously developed Python-based software was used in this study for generation and analysis. Twelve patients with forty-eight total plans with SIB were planned using Rapid arc (2 and 3 arcs) and IMRT (7 and 9 fields) and compared with radiobiological models Lyman, Kutcher, Burman (LKB) and EUD (Equivalent Uniform Dose) along with physical index such as homogeneity index(HI), conformity index(CI) of target volumes. These models' inputs are the dose-volume histograms (DVHs) calculated by the treatment planning system (TPS). The values obtained vary from one model to the other for the same technique and patient. The maximum dose to the brainstem and spinal cord and the mean dose to the parotids were analysed both dosimetrically and radiobiologically, such as the LKB model effective volume, equivalent uniform dose, EUD-based normal tissue complication probability, and normal tissue integral dose. The mean and max dose to target volume with conformity, homogeneity index, tumor control probability compared with treatment times, and monitor units. Rapid arc (3 arcs) resulted in significantly better OAR sparing, dose homogeneity, and conformity. The findings indicate that the rapid arc plan has improved dose distribution in the target volume compared with IMRT, but the tumor control probability obtained for the two planning methods, Rapid arc (3 arcs) and IMRT (7 fields), are similar. The treatment time and monitor units for the Rapid arc (3 arcs) were superior to other planning methods and considered to be standard in head & neck radiotherapy.

2714: Microvasculature-on-chip as radiobiological model for inflammatory response to ionizing irradiation

2714: Microvasculature-on-chip as radiobiological model for inflammatory response to ionizing irradiation

A random sampling method for radiobiological modeling

Various radiobiological models have been proposed to describe the clonogenic cell survival after radiation. Least square model fitting to match experimental survival data can be tedious and sometimes difficult for complex models with multiple parameters. In this work, a random sampling method is developed to optimize the model parameters automatically to achieve best fitting with the experimental data. A computer program is developed to read the experimental data and to calculate the cell survival fractions based on a given mathematical model. Model parameters are randomly sampled, and the model predictions are compared with the experimental data. The mean square error (MSE) is used as an objective function to drive the iterative process. The goodness-of-fit of the linear quadratic (LQ), multitarget (MT), universal survival curves (USC) and multimode (MM) model was compared between the new method and previous studies for melanoma and non–small-cell lung cancer (NSCLC) cell lines. Good fitting was obtained for the MM model with the NSCLC data (MSE = 0.0005), which was better than the fits to the LQ, MT and USC models (MSE = 0.0019, 0.0082, 0.0024, respectively). The parameters used for the LQ, MT and USC models were α = 0.33Gy, α/β = 10Gy, D0 = 1.25Gy, n = 4.22 and DT = 6.2Gy. The parameters for the MM model (n = 4) were D1 = 3.0Gy, D2 = 8.27Gy, and D3 = 2.93Gy and D4 = 10.6Gy. It is concluded that the random sampling method provides a useful tool for the parameterization of radiobiological models for radiation research and clinical applications.

Histology-driven hypofractionated radiation therapy schemes for early-stage lung adenocarcinoma and squamous cell carcinoma

Background and PurposeHistology was found to be an important prognostic factor for local tumor control probability (TCP) after stereotactic body radiotherapy (SBRT) of early-stage non-small-cell lung cancer (NSCLC). A histology-driven SBRT approach has not been explored in routine clinical practice and histology-dependent fractionation schemes remain unknown. Here, we analyzed pooled histologic TCP data as a function of biologically effective dose (BED) to determine histology-driven fractionation schemes for SBRT and hypofractionated radiotherapy of two predominant early-stage NSCLC histologic subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Material and MethodsThe least-χ2 method was used to fit the collected histologic TCP data of 8510 early-stage NSCLC patients to determine parameters for a well-developed radiobiological model per the Hypofractionated Treatment Effects in the Clinic (HyTEC) initiative. ResultsA fit to the histologic TCP data yielded independent radiobiological parameter sets for radiotherapy of early-stage lung ADC and SCC. TCP increases steeply with BED and reaches an asymptotic maximal plateau, allowing us to determine model-independent optimal fractionation schemes of least doses in 1–30 fractions to achieve maximal tumor control for early-stage lung ADC and SCC, e.g., 30, 44, 48, and 51 Gy for ADC, and 32, 48, 54, and 58 Gy for SCC in 1, 3, 4, and 5 fractions, respectively. ConclusionWe presented the first determination of histology-dependent radiobiological parameters and model-independent histology-driven optimal SBRT and hypofractionated radiation therapy schemes for early-stage lung ADC and SCC. SCC requires substantially higher radiation doses to maximize tumor control than ADC, plausibly attributed to tumor genetic diversity and microenvironment. The determined optimal SBRT schemes agree well with clinical practice for early-stage lung ADC. These proposed optimal fractionation schemes provide first insights for histology-based personalized radiotherapy of two predominant early-stage NSCLC subtypes ADC and SCC, which require further validation with large-scale histologic TCP data.

Optimal hypofractionated radiation therapy schemes for early-stage hepatocellular carcinoma

PurposeStereotactic body radiation therapy (SBRT) has been emerging as an efficacious and safe treatment modality for early-stage hepatocellular carcinoma (HCC), but optimal fractionation regimens are unknown. This study aims to analyze published clinical tumor control probability (TCP) data as a function of biologically effective dose (BED) and to determine radiobiological parameters and optimal fractionation schemes for SBRT and hypofractionated radiation therapy of early-stage HCC. Material and MethodsClinical 1- to 5-year TCP data of 4313 patients from 41 published papers were collected for hypofractionated radiation therapy at 2.5–4.5 Gy/fraction and SBRT of early-stage HCC. BED was calculated at isocenter using three representative radiobiological models developed per the Hypofractionated Treatment Effects in the Clinic (HyTEC) initiative. Radiobiological parameters were determined from a fit to the TCP data using the least χ2 method with one set of model parameters regardless of tumor stages or Child-Pugh scores A and B. ResultsThe fits to the clinical TCP data for SBRT of early-stage HCC found consistent α/β ratios of about 14 Gy for all three radiobiological models. TCP increases sharply with BED and reaches an asymptotic maximal plateau, which results in optimal fractionation schemes of least doses to achieve asymptotic maximal tumor control for SBRT and hypofractionated radiation therapy of early-stage HCC that are found to be model-independent. ConclusionFrom the fits to the clinical TCP data, we presented the first determination of radiobiological parameters and model-independent optimal fractionation regimens in 1–20 fractions to achieve maximal tumor control whenever safe for SBRT and hypofractionated radiation therapy of early-stage HCC. The determined optimal fractionation schemes agree well with clinical practice for SBRT of early-stage HCC. However, most existing hypofractionated radiation therapy schemes of 3–5 Gy/fraction are not optimal, higher doses are required to maximize tumor control, further validation of these findings is essential with clinical TCP data.

The sensitivity of radiobiological models in carbon ion radiotherapy (CIRT) and its consequences on the clinical treatment plan: Differences between LEM and MKM models.

Carbon ion radiotherapy (CIRT) relies on relative biological effectiveness (RBE)-weighted dose calculations. Japanese clinics predominantly use the microdosimetric kinetic model (MKM), while European centers utilize the local effect model (LEM). Despite both models estimating RBE-distributions in tissue, their physical and mathematical assumptions differ, leading to significant disparities in RBE-weighted doses. Several European clinics adopted Japanese treatment schedules, necessitating adjustments in dose prescriptions and organ at risk (OAR) constraints. In the context of these two clinically used standards for RBE-weighted dose estimation, the objective of this study was to highlight specific scenarios for which the translations between models diverge, as shortcomings between them can influence clinical decisions. Our aim was to discuss planning strategies minimizing those discrepancies, ultimately striving for more accurate and robust treatments. Evaluations were conducted in a virtual water phantom and patient CT-geometry, optimizing LEM RBE-weighted dose first and recomputing MKM thereafter. Dose-averaged linear energy transfer (LETd) distributions were also assessed. Results demonstrate how various parameters influence LEM/MKM translation. Similar LEM-dose distributions lead to markedly different MKM-dose distributions and variations in LETd. Generally, a homogeneous LEM RBE-weighted dose aligns with lower MKM values in most of the target volume. Nevertheless, paradoxical MKM hotspots may emerge (at the end of the range), potentially influencing clinical outcomes. Therefore, translation between models requires great caution. Understanding the relationship between these two clinical standards enables combining European and Japanese based experiences. The implementation of optimal planning strategies ensures the safety and acceptability of the clinical plan for both models and therefore enhances plan robustness from the RBE-weighted dose and LETd distribution point of view. This study emphasizes the importance of optimal planning strategies and the need for comprehensive CIRT plan quality assessment tools. In situations where simultaneous LEM and MKM computation capabilities are lacking, it can provide guidance in plan design, ultimately contributing to enhanced CIRT outcomes.

P 01 - Radiobiological modelling in carbon ion radiotherapy: How to apply it in clinical practice

P 01 - Radiobiological modelling in carbon ion radiotherapy: How to apply it in clinical practice

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry.

The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT. The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT. Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters. Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter effectively captures the fluctuations in model coefficients α and RBE. Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool.

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

A Review of Numerical Models of Radiation Injury and Repair Considering Subcellular Targets and the Extracellular Microenvironment.

Astronauts in space are subject to continuous exposure to ionizing radiation. There is concern about the acute and late-occurring adverse health effects that astronauts could incur following a protracted exposure to the space radiation environment. Therefore, it is vital to consider the current tools and models used to describe and study the organic consequences of ionizing radiation exposure. It is equally important to see where these models could be improved. Historically, radiobiological models focused on how radiation damages nuclear deoxyribonucleic acid (DNA) and the role DNA repair mechanisms play in resulting biological effects, building on the hypotheses of Crowther and Lea from the 1940s and 1960s, and they neglected other subcellular targets outside of nuclear DNA. The development of these models and the current state of knowledge about radiation effects impacting astronauts in orbit, as well as how the radiation environment and cellular microenvironment are incorporated into these radiobiological models, aid our understanding of the influence space travel may have on astronaut health. It is vital to consider the current tools and models used to describe the organic consequences of ionizing radiation exposure and identify where they can be further improved.

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose-response curve.

Although substantial efforts have been made to build molecular biomarkers to predict radiation sensitivity, the ability to accurately stratify the patients is still limited. In this study, we aim to leverage large-scale radiogenomics datasets to build genomic predictors of radiation response using the integral of the radiation dose-response curve. Two radiogenomics datasets consisting of 511 and 60 cancer cell lines were utilized to develop genomic predictors of radiation sensitivity. The intrinsic radiation sensitivity, defined as the integral of the dose-response curve (AUC) was used as the radioresponse variable. The biological determinants driving AUC and SF2 were compared using pathway analysis. To build the predictive model, the largest and smallest datasets consisting of 511 and 60 cancer cell lines were used as the discovery and validation cohorts, respectively, with AUC as the response variable. Utilizing a compendium of three pathway databases, we illustrated that integral of the radiobiological model provides a more comprehensive characterization of molecular processes underpinning radioresponse compared to SF2. Furthermore, more pathways were found to be unique to AUC than SF2-30, 288 and 38 in KEGG, REACTOME and WIKIPATHWAYS, respectively. Also, the leading-edge genes driving the biological pathways using AUC were unique and different compared to SF2. With regards to radiation sensitivity gene signature, we obtained a concordance index of 0.65 and 0.61 on the discovery and validation cohorts, respectively. We developed an integrated framework that quantifies the impact of physical radiation dose and the biological effect of radiation therapy in interventional pre-clinical model systems. With the availability of more data in the future, the clinical potential of this signature can be assessed, which will eventually provide a framework to integrate genomics into biologically-driven precision radiation oncology.

Radiobiological evaluation of hypo-fractionated breast cancer radiotherapy regimens in comparison with conventional approaches

Abstract Purpose: This study compares tumor control probability (TCP) and normal tissue complication probability (NTCP) across different hypo-fractionated (HypoRT) and conventional breast radiotherapy regimens using radiobiological models. Materials and methods: Computed tomography data from 30 patients with left breast-conserving surgery were used to evaluate three HypoRT regimens (39 Gy and 41·6 Gy in 13 fractions, and 40 Gy in 15 fractions) and a conventional regimen (50 Gy in 25 fractions). Dose-volume histograms (DVHs) were extracted for radiobiological calculations using Equivalent Uniform Dose (EUD) and Poisson models for TCP, and EUD and LKB (Lyman-Kutcher-Burman) models for NTCP. Results: Conventional treatment achieved significantly higher TCP (95%) than all HypoRT regimens (p < 0·001), with no significant differences between HypoRT regimens (p > 0·05. The 39 Gy/13 fraction regimen showed the lowest lung NTCP (p < 0·05). HypoRT regimens had significantly lower NTCP for the lungs and heart compared to the conventional regimen (p < 0·01). TCP and NTCP values from Poisson and LKB models were higher than those from the EUD model (p < 0·01). Conclusion: HypoRT regimens reduced NTCP, with the lowest values in the regime of 39 Gy/13 fractions regimen, though the conventional regimen had higher TCP.

Dose-response modeling and treatment plan assessment with a python software toolkit

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

Obtaining organ-specific radiobiological parameters from clinical data for radiation therapy planning of head and neck cancers

Objective. Different radiation therapy (RT) strategies, e.g. conventional fractionation RT (CFRT), hypofractionation RT (HFRT), stereotactic body RT (SBRT), adaptive RT, and re-irradiation are often used to treat head and neck (HN) cancers. Combining and/or comparing these strategies requires calculating biological effective dose (BED). The purpose of this study is to develop a practical process to estimate organ-specific radiobiologic model parameters that may be used for BED calculations in individualized RT planning for HN cancers. Approach. Clinical dose constraint data for CFRT, HFRT and SBRT for 5 organs at risk (OARs) namely spinal cord, brainstem, brachial plexus, optic pathway, and esophagus obtained from literature were analyzed. These clinical data correspond to a particular endpoint. The linear-quadratic (LQ) and linear-quadratic-linear (LQ-L) models were used to fit these clinical data and extract relevant model parameters (alpha/beta ratio, gamma/alpha, d T and BED) from the iso-effective curve. The dose constraints in terms of equivalent physical dose in 2 Gy-fraction (EQD2) were calculated using the obtained parameters. Main results. The LQ-L and LQ models fitted clinical data well from the CFRT to SBRT with the LQ-L representing a better fit for most of the OARs. The alpha/beta values for LQ-L (LQ) were found to be 2.72 (2.11) Gy, 0.55 (0.30) Gy, 2.82 (2.90) Gy, 6.57 (3.86) Gy, 5.38 (4.71) Gy, and the dose constraint EQD2 were 55.91 (54.90) Gy, 57.35 (56.79) Gy, 57.54 (56.35) Gy, 60.13 (59.72) Gy and 65.66 (64.50) Gy for spinal cord, optic pathway, brainstem, brachial plexus, and esophagus, respectively. Additional two LQ-L parameters d T were 5.24 Gy, 5.09 Gy, 7.00 Gy, 5.23 Gy, and 6.16 Gy, and gamma/alpha were 7.91, 34.02, 8.67, 5.62 and 4.95.Significance. A practical process was developed to extract organ-specific radiobiological model parameters from clinical data. The obtained parameters can be used for biologically based radiation planning such as calculating dose constraints of different fractionation regimens.

Computational approaches in the estimation of radiobiological damage for human-malignant cells irradiated with clinical proton and carbon beams

Purpose:The use of Monte Carlo (MC) simulations capable of reproducing radiobiological effects of ionising radiation on human cell lines is of great importance, especially for cases involving protons and heavier ion beams. In the latter, huge uncertainties can arise mainly related to the effects of the secondary particles produced in the beam-tissue interaction. This paper reports on a detailed MC study performed using Geant4-based approach on three cancer cell lines, the HTB-177, CRL-5876 and MCF-7, that were previously irradiated with therapeutic proton and carbon ion beams. Methods:A Geant4-based approach used jointly with analytical calculations has been developed to provide a more realistic estimation of the radiobiological damage produced by proton and carbon beams in tissues, reproducing available data obtained from in vitro cell irradiations. The MC “Hadrontherapy” Geant4 application and the Local Effect Model: LEM I, LEM II and LEM III coupled with the different numerical approaches: RapidRusso (RR) and RapidScholz (RS) were used in the study. Results:Experimental survival curves are compared with those evaluated using the highlighted Geant4 MC-based approach via chi-square statistical analysis, for the combinations of radiobiological models and numerical approaches, as outlined above. Conclusion:This study has presented a comparison of the survival data from MC simulations to experimental survival data for three cancer cell lines. An overall best level of agreement was obtained for the HTB-177 cells.