Search

Purpose: The respiratory disease COVID-19 reached global pandemic status in 2020. Excessive inflammation is believed to result in the most severe symptoms and death from this disease. Because treatment options for patients with severe COVID-19 related pulmonary symptoms remain limited, whole-lung low-dose radiation therapy is being evaluated as an anti-inflammatory modality. However, there is concern about the long-term risks associated with low-dose pulmonary irradiation. To help quantify the benefit-risk balance of low-dose radiation therapy for COVID-19, we estimated radiation-induced lifetime risks of both lung cancer and heart disease (major coronary events) for patients of different sexes, treated at ages 50 to 85, with and without other relevant risk factors (cigarette smoking and baseline heart disease risk)., Methods and Materials: These estimates were generated by combining state-of-the-art radiation risk models for lung cancer and for heart disease together with background lung cancer and heart disease risks and age/sex-dependent survival probabilities for the U.S., Results: Estimated absolute radiation-induced risks were generally higher for lung cancer compared with major coronary events. The highest estimated lifetime radiation-induced lung cancer risks were approximately 6% for female smokers treated between ages 50 and 60. The highest estimated radiation-induced heart disease risks were approximately 3% for males or females with high heart disease risk factors and treated between ages 50 and 60., Conclusions: The estimated summed lifetime risk of lung cancer and major coronary events reached up to 9% in patients with high baseline risk factors. Predicted lung cancer and heart disease risks were lowest in older nonsmoking patients and patients with few cardiac risk factors. These long-term risk estimates, along with consideration of possible acute reactions, should be useful in assessing the benefit-risk balance for low-dose radiation therapy to treat severe COVID-19 pulmonary symptoms, and suggest that background risk factors, particularly smoking, should be taken into account in such assessments., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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.

Background: Increasing recognition that transthyretin cardiac amyloidosis (ATTR-CA) is much more common than previously appreciated and the emergence of novel disease-modifying therapeutic agents have led to a paradigm shift in which ATTR-CA screening is considered in high-risk populations, such as patients with heart failure with preserved ejection fraction (HFpEF) or aortic stenosis. Radiation risk from 99m Tc-pyrophosphate ( 99m Tc-PYP) scintigraphy, a test with very high sensitivity and specificity for ATTR-CA, has not been previously determined., Methods and Results: Radiation doses to individual organs from 99m Tc-PYP were estimated using models developed by the Medical Internal Radiation Dose Committee and the International Commission on Radiological Protection. Excess future cancer risks were estimated from organ doses, using risk projection models developed by the National Academies and extended by the National Cancer Institute. Excess future risks were estimated for men and women aged 40-80 and compared to total (excess plus baseline) future risks. All-organ excess cancer risks (90% uncertainty intervals) ranged from 5.88 (2.45,11.4) to 12.2 (4.11,26.0) cases per 100,000 patients undergoing 99m Tc-PYP testing, were similar for men and women, and decreased with increasing age at testing. Cancer risks were highest to the urinary bladder, and bladder risk varied nearly twofold depending on which model was used. Excess 99m Tc-PYP-related cancers constituted < 1% of total future cancers to the critical organs., Conclusion: Very low cancer risks associated with 99m Tc-PYP testing suggest a favorable benefit-risk profile for 99m Tc-PYP as a screening test for ATTR-CA in high-risk populations, such as such as patients with HFpEF or aortic stenosis.

Heavy ion-induced carcinogenesis is a challenge for human space exploration, and mechanistically-motivated mathematical models are needed to predict space-relevant low dose-rate risks, which are difficult to measure experimentally, based on data at higher dose rates. We present such a model, which quantifies targeted and non-targeted radiation effects. We fitted it to lung carcinogenesis data in radon-exposed miners and rats, which provide valuable information on carcinogenesis from protracted exposure to densely-ionizing radiation. We generated model-based estimates for the dose-rate-effect, relative to acute exposures, on heavy ion-induced carcinogenesis at doses/dose rates expected during a Mars mission. A small and not statistically-significant dose-rate effect was predicted: 1.00 (95% CI: 0.54, 1.40) for human data and for combined human and rat data 0.93 (0.06, 1.49). Consequently, heavy ion carcinogenesis estimates from moderate/high dose-rate experimental data may be applicable to doses/dose rates relevant for space exploration., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Recently reported studies have associated radiation exposure from computed tomography (CT) scanning with small excess cancer risks. However, since existing medical records were used in these studies, they could not control for reasons for the CT scans and therefore, the results may have been confounded by indication. Here we conducted a study to estimate potential indication bias that could affect hazard ratios for colorectal, lung and female breast cancers by reasons for a CT scan. This involved a retrospective cohort study of electronic records from all patients aged 18-89 years without previous cancer diagnoses, who received at least one CT scan at Columbia University Medical Center in the period of 1994-2014. This investigation is not a study of CT-related cancer risks with adjustment for reasons, but an evaluation of the potential for confounding by indication in such studies. Among 75,968 patients, 212,487 CT scans were analyzed during a mean follow-up of 7.6 years. For colorectal and female breast cancers, no hazard ratio bias estimates for any of the CT reasons reached statistical significance. For lung cancer, significant biases occurred only in patients with unknown CT reasons and in patients with CTs for "abnormal findings" and in those with CTs for cancer- or nodule-related reasons. This retrospective cohort study among adults with ≥1 CT scan evaluates, for the first time, CT reason-specific indication biases of potential CT-related cancer risks. Overall, our data suggest that, in studies of adults who underwent CT scans, indication bias is likely to be of negligible importance for colorectal cancer and female breast cancer risk estimation; for lung cancer, indication bias is possible but would likely be associated with only a small modulation of the risk estimate. Radiat. Res.

Health risks from space radiations, particularly from densely ionizing radiations, represent an important challenge for long-ranged manned space missions. Reliable methods are needed for scaling low-LET to high-LET radiation risks for humans, based on animal or in vitro studies comparing these radiations. The current standard metric, relative biological effectiveness (RBE) compares iso-effect doses of two radiations. By contrast, a proposed new metric, radiation effects ratio (RER), compares effects of two radiations at the same dose. This definition of RER allows direct scaling of low-LET to high-LET radiation risks in humans at the dose or doses of interest. By contrast to RBE, RER can be used without need for detailed information about dose response shapes for compared radiations. This property of RER allows animal carcinogenesis experiments to be simplified by reducing the number of tested radiation doses. For simple linear dose-effect relationships, RBE = RER. However, for more complex dose-effect relationships, such as those with nontargeted effects at low doses, RER can be lower than RBE. We estimated RBE and RER values and uncertainties using heavy ion ( 12 C, 28 Si, 56 Fe) and gamma-ray-induced tumors in a mouse model for intestinal cancer (APC 1638N/+ ), and used both RBE and RER to estimate low-LET to high-LET risk scaling factors. The data showed clear evidence of nontargeted effects at low doses. In situations, such as the ones discussed here where nontargeted effects dominate at low doses, RER was lower than RBE by factors around 2.8-3.5 at 0.03 Gy and 1.3-1.4 at 0.3 Gy. It follows that low-dose high-LET human cancer risks scaled from low-LET human risks using RBE may be correspondingly overestimated.

Purpose: To quantify the risk of radiation-induced second malignancies (SMN) in pediatric patients receiving craniospinal irradiation (CSI) either with 3-dimensional conformal radiation therapy (Conv CSI) or tomotherapy helical intensity modulated radiation therapy (Tomo CSI)., Methods and Materials: A novel predictive model that accounts for short- and long-term carcinogenesis was incorporated into our institutional treatment planning system to quantify the lifetime risk of SMN in incidentally irradiated organs. Five pediatric patients previously treated with CSI were studied. For each case, Conv CSI and Tomo CSI plans were computed. The excess absolute number of SMN was computed for each plan for each patient. For female patients, age was varied to assess its impact., Results: Tomo CSI has a much higher risk than Conv CSI for breast cancer. Tomo has a slightly increased risk for the lung, and conventional has a slightly higher risk for the thyroid. Both techniques have intermediate risks to the pancreas and stomach, and lesser risks to the bladder and rectum. For the breast, the magnitude of the absolute risks varied with age: 14.2% versus 7.4% (Tomo vs Conv) age 5; 16.9% versus 7.6% age 10, and 18.6% versus 8.0% age 15., Conclusions: Tomo has a higher risk for inducing breast and lung second cancers, and when using Tomo-based intensity modulated radiation therapy, care should be taken to avoid incidental radiation to the breast. When planning CSI, one needs to balance these cancer risks against other normal tissue effects., (Copyright © 2016. Published by Elsevier Inc.)

Recent epidemiological studies have suggested that radiation exposure from pediatric CT scanning is associated with small excess cancer risks. However, the majority of CT scans are performed on adults, and most radiation-induced cancers appear during middle or old age, in the same age range as background cancers. Consequently, a logical next step is to investigate the effects of CT scanning in adulthood on lifetime cancer risks by conducting adult-based, appropriately designed epidemiological studies. Here we estimate the sample size required for such studies to detect CT-associated risks. This was achieved by incorporating different age-, sex-, time- and cancer type-dependent models of radiation carcinogenesis into an in silico simulation of a population-based cohort study. This approach simulated individual histories of chest and abdominal CT exposures, deaths and cancer diagnoses. The resultant sample sizes suggest that epidemiological studies of realistically sized cohorts can detect excess lifetime cancer risks from adult CT exposures. For example, retrospective analysis of CT exposure and cancer incidence data from a population-based cohort of 0.4 to 1.3 million (depending on the carcinogenic model) CT-exposed UK adults, aged 25-65 in 1980 and followed until 2015, provides 80% power for detecting cancer risks from chest and abdominal CT scans.

Purpose: The risk of secondary lung malignancy (SLM) is a significant concern for women treated with whole-breast radiation therapy after breast-conserving surgery for early-stage breast cancer. In this study, a biologically based secondary malignancy model was used to quantify the risk of secondary lung malignancies (SLMs) associated with several common methods of delivering whole-breast radiation therapy (RT)., Methods and Materials: Both supine and prone computed tomography simulations of 15 women with early breast cancer were used to generate standard fractionated and hypofractionated whole-breast RT treatment plans for each patient. Dose-volume histograms (DVHs) of the ipsilateral breast and lung were calculated for each patient on each plan. A model of spontaneous and radiation-induced carcinogenesis was used to determine the relative risks of SLMs for the different treatment techniques., Results: A higher risk of SLMs was predicted for supine breast irradiation when compared with prone breast irradiation for both the standard fractionation and hypofractionation schedules (relative risk [RR] = 2.59, 95% confidence interval (CI) = 2.30-2.88, and RR = 2.68, 95% CI = 2.39-2.98, respectively). No difference in risk of SLMs was noted between standard fractionation and hypofractionation schedules in either the supine position (RR = 1.05, 95% CI = 0.97-1.14) or the prone position (RR = 1.01, 95% CI = 0.88-1.15)., Conclusions: Compared with supine whole-breast irradiation, prone breast irradiation is associated with a significantly lower predicted risk of secondary lung malignancy. In this modeling study, fractionation schedule did not have an impact on the risk of SLMs in women treated with whole-breast RT for early breast cancer., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Purpose: To quantify the effect of reduced life expectancy on cancer risk by comparing estimated lifetime risks of lung cancer attributable to radiation from commonly used computed tomographic (CT) examinations in patients with and those without cancer or cardiac disease., Materials and Methods: With the use of clinically determined life tables, reductions in radiation-attributable lung cancer risks were estimated for coronary CT angiographic examinations in patients with multivessel coronary artery disease who underwent coronary artery bypass graft (CABG) surgery and for surveillance CT examinations in patients treated for colon cancer. Statistical uncertainties were estimated for the risk ratios in patients who underwent CABG surgery and patients with colon cancer versus the general population., Results: Patients with decreased life expectancy had decreased radiation-associated cancer risks. For example, for a 70-year-old patient with colon cancer, the estimated reduction in lifetime radiation-associated lung cancer risk was approximately 92% for stage IV disease, versus 8% for stage 0 or I disease. For a patient who had been treated with CABG surgery, the estimated reduction in lifetime radiation-associated lung cancer risk was approximately 57% for a 55-year-old patient, versus 12% for a 75-year-old patient., Conclusion: The importance of radiation exposure in determining optimal imaging usage is much reduced for patients with markedly reduced life expectancies: Imaging justification and optimization criteria for patients with substantially reduced life expectancies should not necessarily be the same as for those with normal life expectancies., (© RSNA, 2011.)

Different types of ionizing radiation produce different dependences of cancer risk on radiation dose/dose rate. Sparsely ionizing radiation (e.g. γ-rays) generally produces linear or upwardly curving dose responses at low doses, and the risk decreases when the dose rate is reduced (direct dose rate effect). Densely ionizing radiation (e.g. neutrons) often produces downwardly curving dose responses, where the risk initially grows with dose, but eventually stabilizes or decreases. When the dose rate is reduced, the risk increases (inverse dose rate effect). These qualitative differences suggest qualitative differences in carcinogenesis mechanisms. We hypothesize that the dominant mechanism for induction of many solid cancers by sparsely ionizing radiation is initiation of stem cells to a pre-malignant state, but for densely ionizing radiation the dominant mechanism is radiation-bystander-effect mediated promotion of already pre-malignant cell clone growth. Here we present a mathematical model based on these assumptions and test it using data on the incidence of dysplastic growths and tumors in the mammary glands of mice exposed to high or low dose rates of γ-rays and neutrons, either with or without pre-treatment with the chemical carcinogen 7,12-dimethylbenz-alpha-anthracene (DMBA). The model provides a mechanistic and quantitative explanation which is consistent with the data and may provide useful insight into human carcinogenesis.

Background: Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages., Methods: We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure., Results: Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site-dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not., Conclusion: For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis become increasingly important as the age at exposure increases. Radiation-induced cancer risks after exposure in middle age may be up to twice as high as previously estimated, which could have implications for occupational exposure and radiological imaging.

Studies of radiation carcinogenesis in animals allow detailed investigation of how the risk depends on age at exposure and time since exposure and of the mechanisms that determine this risk, e.g., induction of new pre-malignant cells (initiation) and enhanced proliferation of already existing pre-malignant cells (promotion). To assist the interpretation of these patterns, we apply a newly developed biologically based mathematical model to data on several types of solid tumors induced by acute whole-body radiation in mice. The model includes both initiation and promotion and analyzes pre-malignant cell dynamics on two different time scales: comparatively short-term during irradiation and long-term during the entire life span. Our results suggest general mechanistic similarities between radiation carcinogenesis in mice and in human atomic bomb survivors. The excess relative risk (ERR) in mice decreases with age at exposure up to an exposure age of 1 year, which corresponds to mid-adulthood in humans; the pattern for older ages at exposure, for which there is some evidence of increasing ERRs in atomic bomb survivors, cannot be evaluated using the data set analyzed here. Also similar to findings in humans, initiation dominates the ERR at young ages in mice, when there are few background pre-malignant cells, and promotion becomes important at older ages.

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track pre-malignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrate a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age.

When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well-characterized doses of a well-studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation, can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze if radiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this IIP model. Combining Monte-Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance>>mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: (a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; (b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.