The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

Laurier, Dominique; Billarand, Yann; Klokov, Dmitry; Leuraud, Klervi

doi:10.1088/1361-6498/acdfd7

Cited by 18 publications

(11 citation statements)

References 99 publications

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“…In the field of radiological protection, a linear no-threshold model (LNT) is currently used to estimate the risk of cancer (and other stochastic effects) after LD of IR (reviewed by UNSCEAR 2021; Laurier et al 2023). Accordingly, some recent epidemiological studies showed an increase in cancer mortality in nuclear industry workers (Richardson et al 2023) or an increase in hematological cancer after a CT-scan (Bosch de Basea Gomez et al 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, some recent epidemiological studies showed an increase in cancer mortality in nuclear industry workers (Richardson et al 2023) or an increase in hematological cancer after a CT-scan (Bosch de Basea Gomez et al 2023). However, other studies suggest other options, such as that the LNT model overestimates the risk of cancer after LD of IR, that there are different slopes of dose-response for LD and high doses, or even that there is a threshold below which no deleterious effects would exist (reviewed by Laurier et al 2023). The present study shows that, after a CT-scan dose, there are genotoxic and molecular effects and that these effects are different depending on the radiosensitivity of the cells.…”

Section: Discussionmentioning

confidence: 99%

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Differential biological effect of low doses of ionizing radiation depending on the radiosensitivity in a cell line model

Palma-Rojo,

Barquinero,

Pérez-Alija

et al. 2024

Preprint

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Purpose Exposure to low doses (LD) of ionizing radiation (IR), such as the ones employed in computed tomography (CT) examination, can be associated with cancer risk. However, not all individuals respond the same to IR, and cancer development could depend on the individual radiosensitivity. Notably, inter-individual differences in the response to IR have been very well studied for high and medium doses, but not for LD. In the present study, we wanted to evaluate the differences in the response to a CT-scan radiation dose of 20 mGy in two lymphoblastoid cell lines with different radiosensitivity. Materials and Methods Several parameters were studied: gene expression, DNA damage, and its repair (by analyzing gamma-H2AX foci, chromosome breaks, and sister chromatid exchange), as well as cell viability, proliferation, and death. Results After 20 mGy of IR, the radiosensitive (RS) cell line showed an increase in DNA damage, and higher cell proliferation and apoptosis, whereas the radioresistant (RR) cell line was insensitive to this LD. Interestingly, gene expression analysis showed a higher expression of an antioxidant gene in the RR cell line, which could be used by the cells as a protective mechanism. After a dose of 500 mGy, both cell lines were affected by IR but with significant differences. The RS cells presented an increase in DNA damage and apoptosis, but a decrease in cell proliferation and cell viability, as well as less antioxidant response. Conclusions A differential biological effect was observed between two cell lines with different radiosensitivity, and these differences are especially interesting after a CT scan dose. If this is confirmed by further studies, one could think that individuals with radiosensitivity-related genetic variants may be more vulnerable to long-term effects of IR, potentially increasing cancer risk after LD exposure.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Differential biological effect of low doses of ionizing radiation depending on the radiosensitivity in a cell line model

Palma-Rojo,

Barquinero,

Pérez-Alija

et al. 2024

Preprint

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“…Even if it is accurate the effects indicated are too low to be shown even by large population epidemiological studies 5 . However, a recent detailed review supports the continued use of the hypothesis (Laurier et al 2023). This does pose a question; if the impact of a collective radiation dose will not show up, even with careful epidemiological studies, should we concern ourselves with it?…”

Section: Costs and Benefits Of Protective Actionsmentioning

confidence: 99%

On the value and costs of protective actions in a nuclear emergency

Pearce

2023

J. Radiol. Prot.

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This note discusses some recent reviews on the costs and benefits of the protective actions we plan to take in the event of a nuclear emergency and considers if there is any need to review current plans in the light of this learning.It finds that the implications of dislocations on mental health have not been taken into account when setting action levels for evacuation and relocation and discusses how this finding should affect the way we determine and communicate protective actions.It suggests a softer messaging when the doses experienced are likely to be a few tens of milli-sievert, a rethink of how to protect the occupants of care homes for the elderly in evacuation zones, and attempts to minimise dislocations possibly by raising the dose thresholds at which they are implemented and reducing the evidence base needed to withdraw protective actions.

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“…One of the consequences of this approach is model selection uncertainty, where the researcher needs to decide which of these models to use to analyze a particular radiation effects dataset, or perhaps use a weighted ensemble of several models 9,10 . Therefore, model-specific differences in assumed dose response shapes and their implications foster controversy [11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Causal Machine Learning Analysis of Radiation-Induced Leukemia and Solid Tumor Incidence in Japanese Atomic Bomb Survivors

Shuryak,

Liu,

Wang

et al. 2024

Preprint

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Uncertainty in low dose ionizing radiation induced health risks stems from several factors. The complex biological pathways leading to diseases like cancer are not fully understood, making it difficult to distinguish the contribution of radiation, particularly at low doses which induce only small perturbations to background disease risks. Additionally, traditional dose response models, such as the Linear No Threshold formalism and competing threshold or hormesis models, impose rigid assumptions on dose response shapes, causing controversy and increasing model selection uncertainty. Furthermore, these modeling strategies operate on the level of correlations/associations, and are not designed to directly address the ultimate goal of radiation epidemiology assessing causal links between radiation and disease. A promising and rapidly developing approach for addressing some of these challenges is causal machine learning (CML), such as double/debiased machine learning (DML), which is designed to model causal effects in multi dimensional data sets. Our study employs DML to elucidate the causal impacts of radiation exposure on the incidence of leukemia, all solid tumors, and stomach tumors among Japanese atomic bomb survivors. Its goal was not to produce a definitive re analysis of these data sets, but to provide a useful example of implementing CML in radiation epidemiology, which can advance the field by supplementing traditional modeling approaches. The results revealed robust positive causal effects of radiation for all three tumor types, especially for leukemia and stomach tumors. The effect magnitudes, and uncertainties, were not dramatically different at low doses than at higher doses. The influences of age at exposure, attained age, sex and other covariates on the causal effects of radiation were assessed using Shapley Additive Explanations (SHAP) values. We believe that this analysis, based on a flexible machine learning framework with a causal inference motivation and without strict dose response assumptions, provides an important contribution to radiation epidemiology.

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The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

Cited by 18 publications

References 99 publications

Differential biological effect of low doses of ionizing radiation depending on the radiosensitivity in a cell line model

Differential biological effect of low doses of ionizing radiation depending on the radiosensitivity in a cell line model

On the value and costs of protective actions in a nuclear emergency

Causal Machine Learning Analysis of Radiation-Induced Leukemia and Solid Tumor Incidence in Japanese Atomic Bomb Survivors

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