This paper updates radiation risk models for leukaemia and lymphoma incidence that can be applied to a variety of adult exposure scenarios and ages of onset of leukaemia and lymphoma, derived from the Life Span Study of atomic bomb survivors of Hiroshima and Nagasaki. Models were developed for four groups of haematological malignancies: acute lymphoblastic leukaemia (ALL), acute myeloblastic leukaemia (AML), chronic myeloid leukaemia (CML), and Hodgkin and non-Hodgkin lymphoma (LYM). Multi-model inference methodology was applied for all groups to construct flexible radiation risk models. The models revealed complex radiation risk patterns with strong dependencies on dose, age, and sex. The ALL group showed high radio-sensitivity with risk decreasing strongly with attained age. The AML group demonstrated a strong non-linear dose response requiring special considerations for fractionated or protracted exposures. The CML model showed a strong dependence on time since exposure. Excess relative risk for the lymphoma group was smaller than other groups and displayed significant sex differences. The developed models for leukaemia and lymphoma provide robust risk predictions for a large variety of adult exposure scenarios with ionising radiation. Multi-model inference proved to be an important tool in the construction of such flexible risk models. The models can be safely used in risk assessment tools or for lifetime risk evaluations.

Background In estimating radiation-associated cancer risks a fixed period for the minimum latency is often assumed. Two empirical latency functions have been used to model latency, continuously increasing from 0. A stochastic biologically-based approach yields a still more plausible way of describing latency and can be directly estimated from clinical data. Methods We derived the parameters for a stochastic biologically-based model from tumour growth data for various cancers, and least-squares fitted the two types of empirical latency function to the stochastic model-predicted cumulative probability. Results There is wide variation in growth rates among tumours, particularly slow for prostate and thyroid cancer and particularly fast for leukaemia. The slow growth rate for prostate and thyroid tumours implies that the number of tumour cells required for clinical detection cannot greatly exceed 10 6 . For all tumours, both empirical latency functions closely approximated the predicted biological model cumulative probability. Conclusions Our results, illustrating use of a stochastic biologically-based model using clinical data not tied to any particular carcinogen, have implications for estimating latency associated with any mutagen. They apply to tumour growth in general, and may be useful for example, in planning screenings for cancer using imaging techniques.

The probability that an observed cancer was caused by radiation exposure is usually estimated using cancer rates and risk models from radioepidemiological cohorts and is called assigned share (AS). This definition implicitly assumes that an ongoing carcinogenic process is unaffected by the studied radiation exposure. However, there is strong evidence that radiation can also accelerate an existing clonal development towards cancer. In this work, we define different association measures that an observed cancer was newly induced, accelerated, or retarded. The measures were quantified exemplarily by Monte Carlo simulations that track the development of individual cells. Three biologically based two-stage clonal expansion (TSCE) models were applied. In the first model, radiation initiates cancer development, while in the other two, radiation has a promoting effect, i.e. radiation accelerates the clonal expansion of pre-cancerous cells. The parameters of the TSCE models were derived from breast cancer data from the atomic bomb survivors of Hiroshima and Nagasaki. For exposure at age 30, all three models resulted in similar estimates of AS at age 60. For the initiation model, estimates of association were nearly identical to AS. However, for the promotion models, the cancerous clonal development was frequently accelerated towards younger ages, resulting in associations substantially higher than AS. This work shows that the association between a given cancer and exposure in an affected person depends on the underlying biological mechanism and can be substantially larger than the AS derived from classic radioepidemiology.

This study aims to identify key anatomic features that govern the individual variability of lung doses from breast-cancer radiotherapy. 3D conformal, intensity-modulated and hybrid techniques with 50.4 Gy whole-breast dose were planned for 128 patients. From their CT images, 17 anatomic measures were assessed and tested as predictors for lung dose-volume characteristics. Tangential techniques yielded mean ipsilateral lung doses in the range of 3-11 Gy. This inter-patient variability was explained to almost 40% by central lung distance, and to almost 60% if this measure was complemented by midplane lung width and maximum heart distance. Also the variability in further dose-volume metrics such as volume fractions receiving 5, 20 or 40 Gy could be largely explained by the anatomy. Multi-field intensity-modulated radiotherapy reduced high-exposed lung volumes, but resulted in higher mean ipsilateral lung doses and larger low-dose burden. Contralateral lung doses ranged from 0.3 to 1 Gy. The results highlight that there are large differences in lung doses among breast-cancer patients. Most of this inter-individual variability can be explained by a few anatomic features. The results will be implemented in a dedicated software tool to provide personalized estimates of long-term health risks related to breast-cancer radiotherapy. The results may also be used to identify favourable as well as problematic anatomies, and serve as a quick quantitative benchmark for individual treatment plans.

Partial body exposure and inhomogeneous dose delivery are features of the majority of medical and occupational exposure situations. However, mounting evidence indicates that the effects of partial body exposure are not limited to the irradiated area but also have systemic effects that are propagated outside the irradiated field. It was the aim of the “Partial body exposure” session within the MELODI workshop 2020 to discuss recent developments and insights into this field by covering clinical, epidemiological, dosimetric as well as mechanistic aspects. Especially the impact of out-of-field effects on dysfunctions of immune cells, cardiovascular diseases and effects on the brain were debated. The presentations at the workshop acknowledged the relevance of out-of-field effects as components of the cellular and organismal radiation response. Furthermore, their importance for the understanding of radiation-induced pathologies, for the discovery of early disease biomarkers and for the identification of high-risk organs after inhomogeneous exposure was emphasized. With the rapid advancement of clinical treatment modalities, including new dose rates and distributions a better understanding of individual health risk is urgently needed. To achieve this, a deeper mechanistic understanding of out-of-field effects in close connection to improved modelling was suggested as priorities for future research. This will support the amelioration of risk models and the personalization of risk assessments for cancer and non-cancer effects after partial body irradiation.

Abstract Women with a history of breast cancer among family members are at increased risk for breast cancer. However, it is unknown whether a familial breast cancer history (FBCH) also increases individual susceptibility to breast cancer from radiation exposure. In this cohort study, 17,200 female Swedish hemangioma patients with 1,079 breast cancer cases diagnosed between 1958 and 2013, exposed to ionizing radiation in infancy, were linked to their first-degree relatives. The association between FBCH and radiation-induced breast cancer risk was assessed. Further, the relevance for breast cancer radiotherapy and mammography screening was evaluated. On average, the radiation-induced excess relative risk and excess absolute risk of breast cancer at age 50 years were 0.51 Gy−1 (95% confidence interval (CI): 0.33, 0.71) and 10.8 cases/10,000 person-years/Gy (95% CI: 7.0, 14.6), respectively. Radiation risk was higher by a factor of 2.7 (95% CI: 1.0, 4.8; P = 0.05) if 1 first-degree relative was affected by breast cancer. For whole-breast standard radiotherapy at age 40 years with a contralateral breast dose of 0.72 Gy, the 20-year radiation-related excess risk of contralateral breast cancer was estimated to increase from 0.6% for women without FBCH to 1.7% for women with FBCH. In a biennial mammography screening program at ages 40–74 years, radiation risk up to age 80 years would increase from 0.11% for women without FBCH to 0.29% for women with FBCH.

In breast cancer radiotherapy, substantial radiation exposure of organs other than the treated breast cannot be avoided, potentially inducing second primary cancer or heart disease. While distant organs and large parts of nearby ones receive doses in the mGy-Gy range, small parts of the heart, lung and bone marrow often receive doses as high as 50 Gy. Contemporary treatment planning allows for considerable flexibility in the distribution of this exposure. To optimise treatment with regards to long-term health risks, evidence-based risk estimates are required for the entire broad range of exposures. Here, we thus propose an approach that combines data from medical and epidemiological studies with different exposure conditions. Approximating cancer induction as a local process, we estimate organ cancer risks by integrating organ-specific dose-response relationships over the organ dose distributions. For highly exposed organ parts, specific high-dose risk models based on studies with medical exposure are applied. For organs or their parts receiving relatively low doses, established dose-response models based on radiation-epidemiological data are used. Joining the models in the intermediate dose range leads to a combined, in general non-linear, dose response supported by data over the whole relevant dose range. For heart diseases, a linear model consistent with high- and low-dose studies is presented. The resulting estimates of long-term health risks are largely compatible with rate ratios observed in randomised breast cancer radiotherapy trials. The risk models have been implemented in a software tool PASSOS that estimates long-term risks for individual breast cancer patients.

Klinik (ausschließlich gynäkologische Untersuchung + Urographie ± Endoskopie)

Introduction: The aim of the present study was to estimate the impact of the addition of internal mammary chain (IMC) irradiation in node-positive left-sided breast cancer (BC) patients undergoing regional nodal irradiation (RNI) and comparatively evaluate excess relative and absolute risks of radiation-induced lung cancer/BC and ischaemic heart disease for intensity-modulated radiotherapy (IMRT) versus 3D conformal radiotherapy (3D-CRT). Methods: Four treatment plans were created (3D-CRT and IMRT –/+ IMC) for each of the 10 evaluated patients, and estimates of excess relative risk (ERR) and 10-year excess absolute risk (EAR) were calculated for radiation-induced lung cancer/BC and coronary events using linear, linear-exponential and plateau models. Results: The addition of IMC irradiation to RNI significantly increased the dose exposure of the heart, lung and contralateral breast using both techniques, increasing ERR for secondary lung cancer (58 vs. 44%, p = 0.002), contralateral BC (49 vs. 31%, p = 0.002) and ischaemic heart disease (41 vs. 27%, p = 0.002, IMRT plans). IMRT significantly reduced the mean cardiac dose and mean lung dose as compared to 3D-CRT, decreasing ERR for major coronary events (64% 3D-CRT vs. 41% IMRT, p = 0.002) and ERR for secondary lung cancer (75 vs. 58%, p = 0.004) in IMC irradiation, without a significant impact on secondary contralateral BC risks. Conclusion: Although IMC irradiation has been shown to increase survival rates in node-positive BC patients, it increased dose exposure of organs at risk in left-sided BC, resulting in significantly increased risks for secondary lung cancer/contralateral BC and ischaemic heart disease. In this setting, the adoption of IMRT seems advantageous when compared to 3D-CRT.

Abstract Background The purpose of this study was to estimate the additional risk of side effects attributed to internal mammary node irradiation (IMNI) as part of regional lymph node irradiation (RNI) in breast cancer patients and to compare it with estimated overall survival (OS) benefit from IMNI. Material and methods Treatment plans (n = 80) with volumetric modulated arc therapy (VMAT) were calculated for 20 patients (4 plans per patient) with left-sided breast cancer from the prospective GATTUM trial in free breathing (FB) and in deep inspiration breath hold (DIBH). We assessed doses to organs at risk ((OARs) lung, contralateral breast and heart) during RNI with and without additional IMNI. Based on the OAR doses, the additional absolute risks of 10-year cardiac mortality, pneumonitis, and secondary lung and breast cancer were estimated using normal tissue complication probability (NTCP) and risk models assuming different age and risk levels. Results IMNI notably increased the mean OAR doses. The mean heart dose increased upon IMNI by 0.2–3.4 Gy (median: 1.9 Gy) in FB and 0.0–1.5 Gy (median 0.4 Gy) in DIBH. However, the estimated absolute additional 10-year cardiac mortality caused by IMNI was <0.5% for all patients studied except 70-year-old high risk patients (0.2–2.4% in FB and 0.0–1.1% in DIBH). In comparison to this, the published oncological benefit of IMNI ranges between 3.3% and 4.7%. The estimated additional 10-year risk of secondary cancer of the lung or contralateral breast ranged from 0–1.5% and 0–2.8%, respectively, depending on age and risk levels. IMNI increased the pneumonitis risk in all groups (0–2.2%). Conclusion According to our analyses, the published oncological benefit of IMNI outweighs the estimated risk of cardiac mortality even in case of (e.g., cardiac) risk factors during VMAT. The estimated risk of secondary cancer or pneumonitis attributed to IMNI is low. DIBH reduces the estimated additional risk of IMNI even further and should be strongly considered especially in patients with a high baseline risk.

Track structure based simulations valuably complement experimental research on biological effects of ionizing radiation. They provide information at the highest level of detail on initial DNA damage induced by diverse types of radiation. Simulations with the biophysical Monte Carlo code PARTRAC have been used for testing working hypotheses on radiation action mechanisms, for benchmarking other damage codes and as input for modelling subsequent biological processes. To facilitate such applications and in particular to enable extending the simulations to mixed radiation field conditions, we present analytical formulas that capture PARTRAC simulation results on DNA single- and double-strand breaks and their clusters induced in cells irradiated by ions ranging from hydrogen to neon at energies from 0.5 GeV/u down to their stopping. These functions offer a means by which radiation transport codes at the macroscopic scale could easily be extended to predict biological effects, exploiting a large database of results from micro-/nanoscale simulations, without having to deal with the coupling of spatial scales and running full track-structure calculations.

ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany.

Recent analyses of the Canadian fluoroscopy cohort study reported significantly increased radiation risks of mortality from ischemic heart diseases (IHD) with a linear dose–response adjusted for dose fractionation. This cohort includes 63,707 tuberculosis patients from Canada who were exposed to low-to-moderate dose fractionated X-rays in 1930s–1950s and were followed-up for death from non-cancer causes during 1950–1987. In the current analysis, we scrutinized the assumption of linearity by analyzing a series of radio-biologically motivated nonlinear dose–response models to get a better understanding of the impact of radiation damage on IHD. The models were weighted according to their quality of fit and were then mathematically superposed applying the multi-model inference (MMI) technique. Our results indicated an essentially linear dose–response relationship for IHD mortality at low and medium doses and a supra-linear relationship at higher doses (> 1.5 Gy). At 5 Gy, the estimated radiation risks were fivefold higher compared to the linear no-threshold (LNT) model. This is the largest study of patients exposed to fractionated low-to-moderate doses of radiation. Our analyses confirm previously reported significantly increased radiation risks of IHD from doses similar to those from diagnostic radiation procedures.

Background Patients with left-sided breast cancer have an increased risk of cardiovascular disease (CVD) after radiotherapy (RT). While the awareness of cardiac toxicity has increased enormously over the last decade, the role of individual baseline cardiac risks has not yet been systematically investigated. Aim of the present study was to evaluate the impact of baseline CVD risks on radiation-induced cardiac toxicity. Methods Two hundred ten patients with left-sided breast cancer treated in the prospective Save-Heart Study using a deep inspiration breath-hold (DIBH) technique were analysed regarding baseline risk factors for CVD. Three frequently used prediction tools (Procam, Framingham and Reynolds score) were applied to evaluate the individual CVD risk profiles. Moreover, 10-year CVD excess absolute risks (EAR) were estimated using the individual mean heart dose (MHD) of treatment plans in free breathing (FB) and DIBH. Results The individual baseline CVD risk factors had a strong impact on the 10-year cumulative CVD risk. The mean baseline risks of the non-diabetic cohort ( n = 200) ranged from 3.11 to 3.58%, depending on the risk estimation tool. A large number of the non-diabetic patients had a very low 10-year CVD baseline risk of ≤1%; nevertheless, 8–9% of patients reached ≥10% baseline 10-year CVD risk. In contrast, diabetic patients ( n = 10) had significantly higher baseline CVD risks (range: 11.76–24.23%). The mean 10-year cumulative risk (Framingham score) following RT was 3.73% using the DIBH-technique (MHD:1.42Gy) and 3.94% in FB (MHD:2.33Gy), after adding a 10-year-EAR of + 0.34%(DIBH) and + 0.55%(FB) to the baseline risks, respectively. Smoking status was one of the most important and modifiable baseline risk factors. After DIBH-RT, the 182 non-smoking patients had a mean 10-year cumulative risk of 3.55% (3.20% baseline risk, 0.35% EAR) as compared to 6.07% (5.60% baseline risk, 0.47% EAR) for the 28 smokers. Conclusion In the present study, all CVD prediction tools showed comparable results and could easily be integrated into daily clinical practice. A systematic evaluation and screening helps to identify high-risk patients who may benefit from primary prevention. This could result in an even higher benefit than from heart-sparing irradiation techniques alone.

Breast cancer, gynecological malignancies and lymphomas are the most frequently diagnosed tumors in pregnant women. The feasibility of radiotherapy during pregnancy remains a subject of debate and clinicians continue to hesitate on this approach, trying to avoid radiotherapy in most cases. Since the 1990s, several technological advances, including intensity modulated and image guided radiation delivery, have been implemented in radiation oncology to improve the radiation treatment in terms of effectiveness and tolerability. It remains uncertain which short- and long-term health effects the radiation exposure of the fetus may have through advanced radiotherapy techniques. The present systematic literature review aims to summarize the limited current evidences of the feasibility and clinical results of "modern" radiotherapy procedures for the treatment of the most frequently diagnosed tumors in pregnant women.

Breast cancer radiotherapy may in the long term lead to radiation-induced secondary cancer or heart disease. These health risks hugely vary among patients, partially due to anatomy-driven differences in doses deposited to the heart, ipsilateral lung and contralateral breast. We identify four anatomic features that largely cover these dosimetric variations to enable personalized risk estimates. For three exemplary, very different risk scenarios, the given parameter set reproduces 63-74% of the individual risk variability for left-sided breast cancer patients. These anatomic features will be used in the PASSOS software to support decision processes in breast-cancer therapy.

The biophysical simulation tool PARTRAC has been primarily developed to model radiation physics, chemistry and biology on nanometre to micrometre scales. However, the tool can be applied in simulating radiation effects in an event-by-event manner over macroscopic volumes as well. Benchmark simulations are reported showing that PARTRAC does reproduce the macroscopic Bragg peaks of proton beams, although the penetration depths are underestimated by a few per cent for high-energy beams. PARTRAC also quantifies the increase in DNA damage and its complexity along the beam penetration depth. Enhanced biological effectiveness is predicted in particular within distal Bragg peak parts of therapeutic proton beams.

Correction to: Strahlenther Onkol 2017 https://doi.org/10.1007/s00066-017-1213-y Unfortunately, during copy editing, the titles of Fig. 2a and 2b were removed. The correct Fig. 2a and 2b are shown below. The original article has been corrected …