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The discovery of X-rays was followed by a variety of attempts to treat infectious diseases and various other non-cancer diseases with ionizing radiation, in addition to cancer. There has been a recent resurgence of interest in the use of such radiotherapy for non-cancer diseases. Non-cancer diseases for which use of radiotherapy has currently been proposed include refractory ventricular tachycardia, neurodegenerative diseases (e.g. Alzheimer's disease and dementia), and Coronavirus Disease 2019 (COVID-19) pneumonia, all with ongoing clinical studies that deliver radiation doses of 0.5–25 Gy in a single fraction or in multiple daily fractions. In addition to such non-cancer effects, historical indications predominantly used in some countries (e.g. Germany) include osteoarthritis and degenerative diseases of the bones and joints. This narrative review gives an overview of the biological rationale and ongoing preclinical and clinical studies for radiotherapy proposed for various non-cancer diseases, discusses the plausibility of the proposed biological rationale, and considers the long-term radiation risks of cancer and non-cancer diseases. A growing body of evidence has suggested that radiation represents a double-edged sword, not only for cancer, but also for non-cancer diseases. At present, clinical evidence has shown some beneficial effects of radiotherapy for ventricular tachycardia, but there is little or no such evidence of radiotherapy for other newly proposed non-cancer diseases (e.g. Alzheimer's disease, COVID-19 pneumonia). Patients with ventricular tachycardia and COVID-19 pneumonia have thus far been treated with radiotherapy when they are an urgent life threat with no efficient alternative treatment, but some survivors may encounter a paradoxical situation where patients were rescued by radiotherapy but then get harmed by radiotherapy. Further studies are needed to justify the clinical use of radiotherapy for non-cancer diseases, and optimize dose to diseased tissue while minimizing dose to healthy tissue. [ABSTRACT FROM AUTHOR]

The lessons learned from the Coronavirus Disease 2019 (COVID-19) pandemic are numerous. Low dose radiotherapy (LDRT) was used in the pre-antibiotic era as treatment for bacterially/virally associated pneumonia. Motivated in part by these historic clinical and radiobiological data, LDRT for treatment of COVID-19-associated pneumonia was proposed in early 2020. Although there is a large body of epidemiological and experimental data pointing to effects such as cancer at low doses, there is some evidence of beneficial health effects at low doses. It has been hypothesized that low dose radiation could be combined with immune checkpoint therapy to treat cancer. We shall review here some of these old radiobiological and epidemiological data, as well as the newer data on low dose radiation and stimulated immune response and other relevant emerging data. The paper includes a summary of several oral presentations given in a Symposium on "Low dose RT for COVID and other inflammatory diseases" as part of the 67th Annual Meeting of the Radiation Research Society, held virtually 3-6 October 2021. [ABSTRACT FROM AUTHOR]

The overall aim of this contribution to the 'Second Bill Morgan Memorial Special Issue' is to provide a high-level review of a recent report developed by a Committee for the National Council on Radiation Protection and Measurements (NCRP) titled 'Approaches for Integrating Information from Radiation Biology and Epidemiology to Enhance Low-Dose Health Risk Assessment'. It derives from previous NCRP Reports and Commentaries that provide the case for integrating data from radiation biology studies (available and proposed) with epidemiological studies (also available and proposed) to develop Biologically-Based Dose-Response (BBDR) models. In this review, it is proposed for such models to leverage the adverse outcome pathways (AOP) and key events (KE) approach for better characterizing radiation-induced cancers and circulatory disease (as the example for a noncancer outcome). The review discusses the current state of knowledge of mechanisms of carcinogenesis, with an emphasis on radiation-induced cancers, and a similar discussion for circulatory disease. The types of the various informative BBDR models are presented along with a proposed generalized BBDR model for cancer and a more speculative one for circulatory disease. The way forward is presented in a comprehensive discussion of the research needs to address the goal of enhancing health risk assessment of exposures to low doses of radiation. The use of an AOP/KE approach for developing a mechanistic framework for BBDR models of radiation- induced cancer and circulatory disease is considered to be a viable one based upon current knowledge of the mechanisms of formation of these adverse health outcomes and the available technical capabilities and computational advances. The way forward for enhancing low-dose radiation risk estimates will require there to be a tight integration of epidemiology data and radiation biology information to meet the goals of relevance and sensitivity of the adverse health outcomes required for overall health risk assessment at low doses and dose rates. [ABSTRACT FROM AUTHOR]

AbstractPurposeConclusionsEpidemiological studies of stochastic radiation health effects such as cancer, meant to estimate risks of the adverse effects as a function of radiation dose, depend largely on estimates of the radiation doses received by the exposed group under study. Those estimates are based on dosimetry that always has uncertainty, which often can be quite substantial. Studies that do not incorporate statistical methods to correct for dosimetric uncertainty may produce biased estimates of risk and incorrect confidence bounds on those estimates. This paper reviews commonly used statistical methods to correct radiation risk regressions for dosimetric uncertainty, with emphasis on some newer methods. We begin by describing the types of dose uncertainty that may occur, including those in which an uncertain value is shared by part or all of a cohort, and then demonstrate how these sources of uncertainty arise in radiation dosimetry. We briefly describe the effects of different types of dosimetric uncertainty on risk estimates, followed by a description of each method of adjusting for the uncertainty.Each of the method has strengths and weaknesses, and some methods have limited applicability. We describe the types of uncertainty to which each method can be applied and its pros and cons. Finally, we provide summary recommendations and touch briefly on suggestions for further research. [ABSTRACT FROM AUTHOR]

Purposes: To ascertain possible evidence for radiosensitive subgroups in the Japanese atomic-bomb survivors., Materials and Methods: Time- and age-adjusted relative risk models were fitted to the Japanese atomic-bomb survivor mortality data, taking account of the modifying effects of acute injury status and adjusting for random dosimetric errors., Results: For leukaemia, there were statistically significantly higher relative risks among those persons reporting either epilation (two-sided p=0.03) or burns (two-sided p=0.02), but after adjustment is made for the effects of dosimetric error these results become statistically non-significant (two-sided p>0.05) For all other endpoints (cancers other than leukaemia, benign neoplasms, cardiovascular disease, non-cancer and non-cardiovascular disease) there were no statistically significant modifying effects on relative risk of acute injury status, whether or not adjustment is made for the effects of dosimetric error., Conclusions: Apart from leukaemia, there are generally no indications for cancer or other disease endpoints of variations in dose-response by acute injury status. The increased risks observed for leukaemia among those reporting epilation or burns are much diminished after adjustment for dosimetric error, and so there is no strong evidence of a sensitive subgroup within the Life Span Study cohort.

Purposes: To model radon-induced lung cancer in uranium miners using a quasi-biological model of carcinogenesis., Materials and Methods: Fitting of generalizations of the stochastic two-mutation carcinogenesis model of Moolgavkar, Venzon and Knudson to a case-control dataset nested within the cohort and to the full cohort of lung cancer mortality in the Colorado Plateau uranium miners, taking account of exposure to cigarette smoke and to radon daughters., Results: Models with three mutations gave adequate descriptions of the time and age patterns of radon-daughter-induced excess lung tumour mortality. The overall fit of the two-mutation model to the case-control data was somewhat worse than that of the three-mutation model. For both the optimal two- and three-mutation models radon daughters and cigarette smoke were assumed to act on the first mutation rate. In the optimal two-mutation model, radon daughters also modified the intermediate cell death or differentiation rate. In the optimal three-mutation model, radon daughters modified the second mutation rate. In all models, the action of radon daughters and cigarette smoke was markedly non-linear, particularly in their action on the mutation rates. The optimal two- and three-mutation models fitted to the cohort data were of slightly different form to those fitted to the case-control data. The model fits to the cohort data are preferred to those to the case-control data on grounds of plausibility., Conclusions: Quasi-biological carcinogenesis models with three mutations give adequate descriptions of the time and age patterns of radon-daughter-induced excess lung tumour mortality. The overall fit of the two-mutation model is somewhat worse than that of the three-mutation model.