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2 Review of NASA Model INTRODUCTION NASA’s proposed space radiation cancer risk assessment model for radiation-induced cancer in astronauts is described in the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties—2010 (Cucinotta et al., 2011). That 2011 NASA report, as it is called hereafter, is divided into discussions of the various compo - nents of the proposed model, including the discussion of the key data sets that either were used to develop the component or are intended as inputs to calculations made by the model. It should be noted that the components of the proposed model are not, in every case, described in the 2011 NASA report. Instead they are incorporated by reference to earlier reports, such as the “BEIR VII Phase 2” report from the National Research Council’s (NRC’s) Committee on Biological Effects of Ionizing Radiation (BEIR) (NRC, 2006) and NCRP Report No. 132, Radiation Protection Guidance for Activities in Low-Earth Orbit (NCRP, 2000). This chapter closely follows the organization of the 2011 NASA report (Cucinotta et al., 2011), with each of the individual model components first reviewed separately below, followed by a review of the integrated model in the chapter’s final text section entitled “Integration and Completeness of the Model.” This review of the integrated model summarizes the present report’s major conclusions and recommendations regarding the model, including those pertaining to the individual components discussed in the chapter. When recommending research that could help improve future versions of the model, the committee primarily considered research that could feasibly be car- ried out in the next 5 years, since the NASA model is generally updated every 5 years. While suggestions appear throughout the discussions in the report, the most important recommendations and conclusions are highlighted in bold in the text, and the majority of these appear in the final section of this chapter. This report does not attempt to duplicate the extensive background and descriptive material contained in the 2011 NASA report, but rather it refers the reader, as appropriate, either to specific sections of the NASA report or to the prior reports incorporated by NASA’s proposed model. SPACE RADIATION ENVIRONMENTS AND TRANSPORT MODELS The assessment of cancer risk due to space radiation begins with defining the external (or ambient) radiation environments. Data describing these environments are inputs to transport calculations to obtain the local radi - ation environment, modified by spacecraft and body shielding, at tissues of concern. Galactic cosmic rays (GCRs) 15
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16 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS and particles from solar particle events (SPEs) are two major components1 of the space radiation environment that pose radiation risk to astronauts during space missions away from the protective zone of Earth’s magnetic field. The GCR and SPE environments in the solar system have a strong correlation with the approximately 11-year solar cycle. Galactic Cosmic Rays Overview Note that much of the material in this Overview section is contained in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth, in the chapter by J.R. Jokipii (2010).2 Galactic cosmic rays constitute a major part of the space radiation environment near Earth. GCRs are very energetic charged particles (electrons and atomic nuclei) that are believed to be accelerated by vast, spheroidal blast waves from supernova explosions that propagate in the interstellar gas. The accelerated cosmic rays enter the heliosphere on their way to the inner solar system and Earth. In the process they are changed, and so understanding their transport is essential to understanding the space radiation environment. The heliosphere is a vast spheroidal cavity in the local interstellar plasma, some 150 to 200 astronomical units (AU) in size, created by a supersonic, radial flow of plasma, called the solar wind, that flows outward from the Sun. The spatial scale of the heliosphere is determined by both the Sun and the back pressure of the surround - ing interstellar plasma and magnetic field. Far from the Sun, the outward-flowing solar wind is spread over such a large volume that it can no longer continue out into the surrounding interstellar plasma. Because the wind is flowing supersonically (faster than waves can propagate), the supersonic flow ends at a spheroidal shock wave, which is called the heliospheric termination shock, where the flow changes suddenly to a subsonic (slower than the wave speed) outward flow. The interstellar plasma is moving at about 26 km/sec relative to the heliosphere, pushing it in on one side. Beyond the termination shock, the solar plasma continues to flow outward, but it is deflected and eventually turns to flow in the same direction as the interstellar plasma, forming a large, trailing, heliospheric tail. The inter stellar medium also contains neutral atoms, and these also play a role in the interaction of the heliosphere with the inter- stellar medium, although the effects are small and may be neglected. Energetic particles including cosmic rays pervade the heliosphere, as they do all regions of low-enough density in the universe. The energetic particles are in four basic types: galactic cosmic rays, anomalous cosmic rays, interplanetary energetic particles, and solar energetic particles. This discussion concentrates primarily on galactic cosmic rays. They come from the galaxy, where they are thought to be accelerated by supernova blast waves. They envelop the heliosphere with a very nearly constant, isotropic bath. The particles are then partially excluded from the inner parts of the heliosphere. Therefore, their intensity reflects the varying properties of the heliosphere. GCRs have a typical energy of 1 GeV and are present continuously, but fluctuate on a variety of timescales. Solar cosmic rays are produced sporadically by the Sun, at considerably lower energies than those of the galactic particles (see Figure 2.1). Their spectrum is also a much more rapidly decreasing function of energy. The time-averaged intensity of these two types of cosmic rays, as a function of energy, is illustrated in Figure 2.1, where the solar particles are a solar-cycle average. The average spectrum over time is therefore dominated by the GCRs, although for short periods (hours to days) the solar particles can be quite intense. The intensity of GCRs in the inner solar system is observed to vary with time over a wide variety of timescales. The time variations of galactic particles are due to variations in the solar wind and its entrained magnetic field, which are accessible to direct measurement. There exists a generally accepted physical model that can account quantitatively for these modulations. 1Trapped-particle models are not covered here because they contribute very little to the organ dose for missions aboard the International Space Station or missions to the Moon or to Mars. 2J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge University Press, New York, 2010. Copyright © 2010 Cambridge University Press. Used with permission.
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17 REVIEW OF NASA MODEL FIGURE 2.1 The observed intensity of cosmic rays at Earth orbit at quiet times. Shown are galactic cosmic-ray protons and helium. The dashed line is a time average of solar energetic particles. SOURCE: J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge University Press, New York, 2010. Copyright © 2010 2-1 Figure Cambridge University Press. Reprinted with permission. Bitmapped It is reasonably certain that cosmic-ray variations on timescales of less than about 50,000 years must be caused by changes in the heliosphere resulting from changes in the Sun. Interstellar variations over longer time periods can be caused either by the motion of the solar system through the interstellar medium or by transient changes in the interstellar cosmic-ray intensity caused by changes in local interstellar conditions such as by a supernova blast wave. In addition, the heliospheric structure, and hence its effects on cosmic rays, can be affected by changes in the interstellar medium caused by, for example, interstellar clouds. The largest observed periodic variation is the variation of GCRs in anti-phase with the 11-year sunspot cycle. The variation of the GCR intensity over the past five sunspot cycles is illustrated in Figure 2.2. Note the very obvious 11-year cyclic variation and the alternating shapes of successive cosmic-ray maxima. At this time it is not completely understood how solar activity changes the interplanetary medium to produce the observed temporal variations. There are four principal elements: 1. Co-rotating, high-speed streams produce nearly periodic variations at the solar rotation period of approxi - mately 27 days. 2. Coronal mass ejections (CMEs) and high-speed solar wind streams combine to produce large-scale, long-lived structures (known as global merged interaction regions, or GMIRs) of enhanced magnetic field that propagate out to the termination shock and into the heliosheath. There is a strong correlation between the rate of CMEs and sunspot numbers that have been observed over periods of high and low solar activity. From this one may conclude that the almost 400 years of sunspot observations provide a useful tool for studying the levels of solar activity over that time.
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18 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS FIGURE 2.2 The modulation of galactic cosmic rays during five sunspot cycles. Top: The intensity of cosmic rays as measured by the Climax neutron monitor as a function of time since 1951. Bottom: The sunspot index as a function of time since 1950. Note the alternation of the cosmic-ray maxima between sharply peaked and more-rounded shapes. This corresponds to the change in the direction of the solar magnetic field. SOURCE: J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Figure 2-2 Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge All type is outlines University Press, New York, 2010. Copyright © 2010 Cambridge University Press. Reprinted with permission. (prints ne but mostly cannot be edited) 3. Changes in magnitude of the heliospheric magnetic field (HMF) occur over many scales (see Section 9.2 of Jokipii, 2010). The gyroradii of cosmic rays are inversely dependent on the strength of the HMF, and this, together with the observed turbulence of interplanetary plasma, produces changes in the cosmic-ray diffusion coefficients that are approximately inversely proportional to the magnetic field magnitude B (see Section 9.4 of Jokipii, 2010). 4. The changing inclination of the heliospheric current sheet that changes from a nearly flat configuration in the equatorial plane at solar minimum to a 90° inclination at solar maximum and then with decreasing solar activity returns to its near equatorial position at the next solar minimum (see Figure 14 of Jokipii, 2010). This is associated with a change in magnetic polarity, leading to a 22-year solar magnetic cycle. Review of the NASA GCR Model The GCR model used in NASA’s proposed model (see Cucinotta et al., 2011) is based on Badhwar-O’Neill (O’Neill, 2006). The model uses a simplified version of the currently well established paradigm of the effects of the heliosphere GCRs. Since the GCRs come from the interstellar medium, this complete model is quite complicated and detailed, involving the spatial and temporal variations of the solar wind, its entrained magnetic field, and the boundary conditions at the interface, which is at a distance from the Sun of some 130 AU. The outer parts of this model are still being debated. However, the basic picture for the inner heliosphere (out to several astronomical units) seems to be well grounded. The simplified GCR model being used in NASA’s proposed model is often used by scientists in the field to cat - egorize the observations of GCRs. The modulation at Earth as a function of time is represented by one parameter, Φ, called the modulation parameter. For convenience, it is expressed as an energy. This parameter, of course, is a
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19 REVIEW OF NASA MODEL quite crude representation of the complicated physical situation, but it can be shown to account reasonably well for observations. The observational data seem to be consistent and generally accepted. Research on how to incorporate more of the physics, such as the 22-year magnetic cycle, into the cancer risk assessment model would make the estimates of risk more accurate. It is not clear at this point, however, whether the extra effort, which would indeed make the uncertainties less, would make enough difference to reduce the overall risks significantly. It would, however, make the sources of uncertainty more certain. Also, incorporating the new understanding resulting from the recent (2009- 2011) historically deep solar minimum will certainly decrease the uncertainties, but, again, it is not clear that the improvement is enough to warrant the effort. The primary uncertainty in the GCR environment calculation comes from a lack of understanding of the physical conditions of transport and the use of statistically based models. The committee also points out that the differences in the GCR intensity at Mars relative to Earth are less than other uncertainties, and so using Earth-based models to address doses at Mars introduces very little uncertainty. Having just passed through an unexpected historically deep solar minimum, it is clear that environmental uncertainties increase with time over years to decades. The recent incorporation of the CREME96 (Tylka et al., 1997) and Nymmick (Nymmick et al., 1996) models adds more complication, but basically tweaks the Badhwar-O’Neill model to try to make more physical the varia - tion of the modulation parameter Φ. However, there is no simple parameterization of the current models that yields the 22-year effects except in an ad hoc manner. The NASA GCR model could be improved by incorporating the 22-year-cycle variation. Solar Particle Events Overview “Solar energetic particle” (SEP) and “solar particle event” (SPE) are names for a very energetic process and for the potentially damaging situation that occurs when very strong magnetic fields in the solar photosphere reach a critical instability. These are also called solar proton events as protons are the most abundant (>90 percent) species in SPEs. There are also substantial radio bursts, X-ray and gamma-ray emissions accompanying SPEs. A schematic illustrating the timescales for the different emissions from solar events is presented in Figure 2.3 (the 27-day period is the approximate rotation rate of the Sun as seen from Earth). SPEs are typically divided into two classes: (1) gradual events, which are typically the largest events and are associated with coronal mass ejection shocks and typically last for several days; and (2) impulsive events (often called solar flares), which are short, longitudinally localized events on the solar surface. There are usually fewer than 10 gradual events a year. The particle abundances in these events are similar to the composition of the solar corona. They have sharp rise times and decay slowly over hours to days and cover a large longitudinal extent. The impulsive events, associated with instabilities in the solar atmosphere, are rich in heavy ions and show a sharp peak in X rays and gamma rays. These two types of events are illustrated in Figure 2.4. SPEs are stochastic in nature. Their characteristics—com - position, intensity, energy spectra, and temporal profile—are highly variable. Typical SPEs are known to pose a small health risk to astronauts and can be effectively attenuated by using relatively thin shield materials, although they can influence mission planning or interfere with mission activities such as extravehicular activities (EVAs). However, large SPEs can be lethal, although they are rare. The intensity and number of very large solar events (CMEs) vary dramatically from solar cycle to solar cycle. The lack of correlation between sunspots and SPEs illustrates the difficulty in reliably predicting the level or fre - quency of activity for the future. One fact is clear, however: SPEs occur far more frequently during solar maximum (Figure 2.5). The energy spectra vary significantly from event to event, as illustrated in Figure 2.6. The energy spectrum of an SPE is an important consideration for accurate radiation risk assessment, but it is also not predict - able at the present time. Feynman and Gabriel (1988) assume a radial dependence of r −3 for the flux inside 1 AU and a radial dependence of r−2 outside 1 AU. This implies that the fluence and dose will be trajectory-dependent for interplanetary missions such as exploration missions to near-Earth objects or to Mars. However, the validity of this scaling law is in question, because there are very few simultaneous measurements of SPEs at Earth and at
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20 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS X-RAY EMISSION SUNLIT IONOSPHERIC DISTURBANCE HF INTERFERENCE RADIO NOISE EMISSION MAJOR IONOSPHERIC PROTON ARRIVAL DISTURBANCE AT EARTH PCA EVENT 10001001010.1 ENERGY (MeV) SOLAR PLASMA 27 MAGNETIC STORM DAYS 0.01 0.1110100 1000 HOURS 1 MIN 10 MIN 1 D AY 10 DA YS FIGURE 2.3 Schematic plot of the relative variations in time of the amplitudes of the X-ray, radio noise, high-energy particle, and solar plasma fluxes for a “typical” large solar flare. NOTE: HF, high frequency; PCA, polar cap absorption. SOURCE: Reprinted from M.A. Shea and D.F. Smart, History of solar proton event observations, Nuclear Physics B (Proc. Suppl.) 39A: 16-25, 1995. Copyright 1996, with permission from Elsevier. 2-3 Figure 10-2 10-3 Particles (#/cm 2 /ster/s/MeV) 10-4 CME 10-5 10-6 339 340 341 342 343 10-2 10-3 FLARE 10-4 10-5 10-6 225 226 227 228 229 Day in 1981 FIGURE 2.4 Two types of solar proton events: (1) gradual events, which are typically the largest events and are associated with coronal mass ejection (CME) shocks and typically last for several days; and (2) impulsive events (often called solar flares), which are short, longitudinally localized events on the solar surface. SOURCE: J. Barth, NASA Goddard Space Flight Center, Figure 2-4 “Modeling Space Radiation Environments,” presentation at the IEEE Nuclear and Space Radiation Effects Conference Short Labels editable Course: Applying Computer Simulation Tools to Radiation Effects Problems, July 21, 1997. graph is bitmapped
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21 REVIEW OF NASA MODEL FIGURE 2.5 Yearly event fluences for protons of energy >30 MeV versus year relative to sunspot maximum. SOURCE: J. Feynman, G. Spitale, and J. Wang, Interplanetary proton fluence model, Journal of Geophysical Research 98:13281-13294, 1993. Copyright 1993 American Geophysical Union. Reproduced by permission of American Geophysical Union. FIGURE 2.6 Energy spectra of several large solar energetic particle (SEP) events. SOURCE: R.A. Mewaldt, C.M.S. Cohen, A.W. Labrador, R.A. Leske, G.M. Mason, M.I. Desai, M.D. Looper, J.E. Mazur, R.S. Selesnick, and D.K. Haggerty, Proton, helium, and electron spectra during the large solar particle events of October-November 2003, Journal of Geophysical Research 110:A09S18, 2005. Copyright 2005 American Geophysical Union. Reproduced by permission of American Geophysical Union. Figure 2.6 All type is outline
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22 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS other locations in the heliosphere. Simply stated, there is no clear understanding of the radial scaling law of SPE flux at the present time. In addition to a high flux of protons, solar flare events also typically are accompanied by small but variable amounts of heavy ions. However, previous estimates (Kim et al., 1999; NCRP, 1989) indicate that solar heavy ions do not contribute much to the SPE radiation hazard to astronauts. Review of the NASA SPE Model The SPE model used in NASA’s proposed space radiation cancer risk assessment model (Cucinotta et al., 2011) is described in Kim et al. (2009, 2011). This SPE model was developed using an extensive events list compiled from King (1974), Feynman et al. (1990), and Shea and Smart (1990) for Solar Cycles 19 to 21, and geo stationary operational environmental satellite (GOES) data from 1986 to the present (Solar Cycles 22 and 23). Also, the database includes the SPE events estimated using impulsive nitrate enhancements in polar ice cores (McCracken et al., 2001). From this events list, NASA used non-homogeneous Poisson statistics (Parzen, 1967) for describ - ing SPE frequency distribution and a hazard function from survival analysis to estimate the expected number of events in a given mission duration. Then, from the prediction of the mean number of SPEs with integral fluence (i.e., Φ30) exceeding given thresholds, a (e.g., a = 107, 108, and 2 × 109 cm−2), the cumulative probabilities of SPE frequencies and total proton fluences are defined at various percentiles during a given mission duration. As for the energy spectrum of the expected SPEs, the event-integrated fluence above an arbitrary energy level of E MeV from the 34 historically largest SPEs is fit with the Weibull function up to 1 GeV. As described above, the new NASA SPE model uses a comprehensive collection of historical SPEs from an extensive database using rigorous mathematical analysis and formulation to determine the total proton fluence and energy spectrum for a given mission length. The new NASA SPE model is an advance over the past model, in which NASA used the fixed event fluence and spectrum from the 1972 August SPE. However, it should be noted that the new model is a statistical SPE model from a data set constructed from past measurements, although the new NASA SPE model is called a probabilistic risk assessment (PRA) model in the NASA report (Cucinotta et al., 2011). The model is not a predictive model for the future, and one must be cautious in extrapolating from the past and present conditions to the future. For both GCR and SPE, the radiation environment at the present time appears to be relatively mild, which is unusual from the historical perspective (NRC, 2006). The new NASA SPE model can be considered to be a major step forward compared to the current NASA SPE model. Nonetheless there are a few minor areas that NASA could consider further addressing in the future, as follows: • The new NASA SPE model is constructed using a list of events from past measurements. However, the definition of an event is not provided in the 2011 NASA report. Depending on how an event is defined, the under- lying distribution function of the events can be different from what is used in the NASA SPE model. There are three areas of SPE climatology research: how to count events that may be correlated, statistics of extreme events, and how SPE characterization varies with the solar cycle. The largest (most extreme) impacts are associated with “clusters” of high-speed CMEs called fast CMEs. Recent studies (Ruzmaikin et al., 2011a,b) indicate that extreme space weather events (i.e., large CMEs and SPEs) are not independent of one another. The most important SPEs for estimating radiation risk to astronauts are these extreme events. A new advanced statistical method (e.g., see Ruzmaikin et al., 2011b, and references therein) recently developed can be used to describe these extreme events. It may change the functional form of the distribution of extreme SPEs, which in turn can affect spacecraft design or mission planning. It will be useful if NASA evaluates the statistics of SPEs using different methods to define better the tail distribution of SPEs, that is, extreme SPEs. Furthermore, since SPE statistics vary dramatically from solar cycle to solar cycle, combining all SPE observations together and saying that they are representative of any given solar cycle introduces additional uncertainty. In this regard, it may be useful for NASA to further investigate the SPE frequency relationship to solar cycles. This may also affect the statistics of extreme events. • Extension of the energy spectrum of an SPE to 1 GeV using the data only up to approximately 100 MeV seems oversimplified, especially when it has been stressed multiple times that the shape of energy spectrum is an
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23 REVIEW OF NASA MODEL important parameter when assessing radiation risk from SPEs. Additional measurements of high-energy protons, especially in the energy range of >100 to >500 MeV, will help better define the energy spectra of SPEs. • There is no mention in the 2011 NASA report of radial and longitudinal variations of the SPE fluence and energy spectrum (e.g., the SPE environments at Earth and at Mars are expected to be different when Earth and Mars are at different solar latitude and longitude). The SEPs preferentially move along the spiral interplanetary magnetic field, and this produces a characteristic coupled longitudinal and radial dependence of the intensity, which depends on the parameters and is difficult to precisely determine. The incorporation of these radial and longitudinal varia - tions of SPE environment may be important for a future mission to Mars. In this regard, actual measurements of SPE environment at Mars would be very useful. The Mars Science Laboratory (MSL) Radiation Assessment Detector (RAD) can provide very valuable data in this sense, which will measure the surface radiation environ - ment at Mars. Furthermore, simultaneous measurements of SPEs on orbit at Mars and at the surface are desired because they can be used to validate radiation transport models for the martian atmosphere and surface. • There is no mention of a heavy-ion component of SPEs in the 2011 NASA report. Although the committee understands that the SPE heavy-ion flux is low compared to the SPE proton flux, the biological effects of heavy ions is very uncertain at this stage, and it may be premature to exclude the SPE heavy-ion consideration entirely from the overall cancer risk assessment for astronauts. To have a complete SPE model, it is suggested that NASA add a heavy-ion component to the new SPE model. There are other SPE models available in the community. However, in terms of data sources and model outputs, NASA’s proposed model is not much different from other models, although each uses a different mathematical approach: the Jet Propulsion Laboratory model uses a lognormal fit to the observed SEP event fluences (Feynman et al., 1993, 2002), and the emission of solar protons model uses the maximum entropy principle (Xapsos et al., 2004). Transport Model Overview The external radiation environments described above change their properties (in terms of particle type and energy spectra) as they go through spacecraft materials and the body mass surrounding the internal organs. Nuclear interactions between the primary radiation and shielding materials can generate a score of secondary particles through spallation or fragmentation reactions, which include secondary neutrons. These interactions are typically modeled using radiation transport codes, which can employ deterministic or Monte Carlo methods. Review of the NASA Shielding Transport Models The transport model used in NASA’s proposed model is HZETRN, high charge and energy transport code (and BRYNTRN, a computational model of baryon transport) (Slaba et al., 2010a,b). The fluence for each particle type and energy at each tissue of interest (behind spacecraft and body shielding) is characterized by using radiation trans - port codes: HZETRN with a quantum multiple scattering fragmentation (QMSFRG) database for GCRs and baryon transport model (BRYNTRN) for SPEs. These radiation transport codes solve the Boltzmann transport equation with the straight-ahead approximation. This is a deterministic approach. Typically, deterministic codes are used for simple geometries for which the Boltzmann equation can be solved numerically—Cartesian, cylindrical, spherical, or toroidal geometries. For complex geometries (e.g., spacecraft and the human body) for which the Boltzmann equa - tion cannot be solved numerically, a Monte Carlo approach is appropriate and would provide more accurate results. However, Monte Carlo simulations take prohibitively long computation time, especially for problems with complex geometry, to obtain the results with a good statistical accuracy. Furthermore, the level of accuracy from the Monte Carlo simulations tends to be negated anyway when a simplified geometry is used in the Monte Carlo simulations. To deal with this situation, NASA uses the aforementioned deterministic codes with ray-tracing techniques to consider a very detailed geometry in a one-dimensional approach. This approach, along with the numerical techniques used in the proposed NASA transport model, is well accepted in the radiation transport community.
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24 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS Based on the presentations given by NASA to the committee during the first meeting and materials provided subsequently, it is the committee’s opinion that the NASA radiation transport codes have been verified and validated through thin and thick target experimental data and by means of intercomparisons among transport codes widely used in the radiation transport community (HETC-HEDS, FLUKA, PHITS, MANPX, and Geant4). Furthermore, the radiation transport codes used in NASA’s proposed space radiation cancer risk assessment model are shown to provide good agreement in most cases with spaceflight measurements from the International Space Station (ISS) and/or Space Transportation System (STS). In this regard, it is concluded that there have been reasonable advances in developing the predictive capability of radiation transport codes used in the NASA cancer risk assess - ment. However, it is noted that comparisons of HZETRN with spaceflight measurements still show greater than 20 percent difference for several occasions (see Tables 2.5 and 2.6a in the 2011 NASA report). As discussed in the 2011 NASA report (Cucinotta et al., 2011), a minor source of discrepancy between trans - port code predictions and measurements is the mesons, electrons, and gamma rays that become important con - tributors to organ doses for very thick shielding—for example, >50 g/cm2 shielding. In this sense, the continuous collection of data for different shielding materials and different ion beams is recommended for further validation of transport code, especially for thick targets. The 2011 NASA report also states that the cross-section data are sparse for some projectile-target combinations, especially above 1,000 MeV/u, and improvements are required in how differential cross sections are represented in transports. The committee agrees with this point and suggests that NASA continue compiling experimental thick-target data for code validation . Final Comments In general, the committee agrees that the uncertainty associated with the space physics parameters (i.e., environments and transport models) is a minor contributor to the overall space radiation cancer risk assessment, within 15 percent for effective dose comparisons. The knowledge, or lack of it, about the biological effects and responses to space radiation is the single most important factor limiting the prediction of radiation risk associated with human space exploration. NASA’s proposed space radiation cancer risk assessment model assigns a slightly higher overall physics model uncertainty than the estimate of 15 percent. It is assumed in the proposed model that light ion (Z ≤ 4) fluence spectra computed at targets of interest would have a normal distribution with a mean shifted to higher value (M = 1.05) and the standard deviation of 0.33, compared to heavy ions ( Z > 5) for which the mean and the standard deviation are assigned to be 1.0 and 0.25, respectively. However, it should be noted that the uncertainty of the space physics model is used independently from other uncertainties in the overall radiation risk assessment. Hence, in the Monte Carlo calculations in the overall risk assessment, tissue-specific particle spectra are being used simply as an input. The radiation environment and transport models used in NASA’s proposed space radiation cancer risk assess - ment model are considered to be a major step forward compared to previous models used (especially the statisti - cal SPE model). The models described in the 2011 NASA report have been developed making extensive use of available data and rigorous mathematical analyses. The uncertainties conservatively allocated to the space physics parameters are deemed adequate at this time, considering that the space physics uncertainty is only a minor con - tributor to the overall cancer risk assessment. Although further research in this area can reduce the uncertainty, it is not clear at this point whether the extra effort would make enough difference in the space physics uncertainty to reduce the overall risks. CANCER RISK PROJECTION MODELS Overview The cancer risk projection component of NASA’s proposed model can be broadly defined as an amalgamation of approaches developed by the 2006 United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2006) and the BEIR VII committee. These committees agree in many respects on how to project lifetime cancer risks following radiation exposure. They both use the latest Japanese atomic bomb survivors life
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25 REVIEW OF NASA MODEL span study (LSS) as the primary data source. Organ-specific doses are combined with organ-specified cancer risk models, and risks are estimated using both excess relative risk (ERR) and excess absolute risk (EAR) models, which are then combined using a weighted average. (The excess relative risk is the cancer rate in the exposed population divided by the rate in the unexposed population minus 1, whereas the excess absolute risk is the cancer rate in the exposed population minus the rate in the unexposed population.) There are, however, a number of areas in which BEIR VII and UNSCEAR differ in their approach, and these can have a non-negligible impact on risk estimates. The areas are these: 1. The functional form of the ERR and EAR risk models; 2. The estimation of cancer mortality risks; 3. The weights used in the weighted average of the ERR and EAR models, which is the approach used to transfer risk estimates from the Japanese to the U.S. population; and 4. Application of a dose and dose rate effectiveness factor (DDREF) to account for potential differences in cancer risk at low doses and low dose rates. NASA’s proposed model broadly follows UNSCEAR (2006) for the first area listed above, BEIR VII (NRC, 2006) for the second and third, and for the fourth it uses a distribution developed for the National Institutes of Health (NIH) radioepidemiological tables (NIH, 2003). The resulting risk of exposure-induced death (REID) estimates from NASA’s proposed model are quite different from those from the current NASA model (developed in 2005). It is difficult, however, to isolate the factors that drive this difference, because NASA’s proposed model combines different components of several existing approaches. The UNSCEAR and BEIR VII committees developed their approaches each following a unified philosophy to risk estimation. For example, BEIR VII developed functional forms for the risk models and a specific “LSS DDREF” approach to be combined with those models. Conversely, UNSCEAR evaluated linear-quadratic models for the LSS data as a direct approach to incorporating evidence of lower risks at lower doses. Furthermore, those reports had different emphases. The UNSCEAR report emphasized cancer mortality results, whereas the BEIR VII report emphasized cancer incidence. More effort, therefore, was put into developing the models and evaluating the models for cancer incidence in the BEIR VII report and vice versa for the UNSCEAR report. NASA’s proposed model combines the UNSCEAR incidence risk models with the BEIR VII mortality approach, which are probably the least developed aspects of the respective reports. The following of either one of these reports more directly by NASA and the providing of careful justification in its published model description for any minor deviations may result in a model that is more transparent and robust. A detailed review of the key aspects of the proposed cancer risk projection component of NASA’s model is given below, including a comparison with the approach of NASA’s current model. Cancer Mortality Risk Estimation NASA limits an astronaut’s radiation exposures to amounts expected to result in no more than a 3 percent excess risk of exposure-induced death. Therefore, a risk projection model for cancer mortality—as opposed to cancer incidence—is required. The traditional approach to estimating risks of cancer mortality is to use risk coefficients estimated directly from the LSS cancer mortality data. A major proposed change in NASA’s proposed model is to use the “incidence-mortality” approach developed by BEIR VII (NRC, 2006) whereby risk coefficients from LSS cancer incidence data are used and then cancer mortality risks are estimated from these incidence risks. For the ERR models the incidence-ERR coefficients are combined directly with current U.S. cancer mortality rates, whereas for the EAR models the EAR cancer incidence coefficients are multiplied by the current ratio of cancer mortality to cancer incidence in the U.S. population. As mentioned earlier these are then combined to provide a single estimate from a weighted average (more details are provided below). This ratio is supposed to approximate the mortality probability for this cancer site. The incidence-mortality approach is considered an improvement for site-specific cancer mortality estima - tion because LSS site-specific cancer incidence data are likely to be more accurate than are cancer mortality data (Ron et al., 1994a,b), which suffer from misclassification of causes on death certificates. However, some further
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42 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS high-LET and low-LET radiations at the molecular and higher levels and there are possibly also different spectra of cancer types and latency, the committee agrees that simple scaling of risks is reasonable, given the current limited state of knowledge of qualitative differences in health effects (see also the section below entitled “Key Element: Other Issues”). The main radiation quality parameter is Z*2/β2, and this largely takes the place of LET, although three addi- tional parameters (κ, Σ0/αγ, and m) are also introduced by Equation 2.2 above to define P(Z,E) in the quality factor relationships for risk as a function of Z*2/β2. According to Equation 5.21 of the 2011 NASA report (Cucinotta et al., 2011): 6.24�Σ0��γ� �� � �� � ���� ��� � � ���� �� ��� . (2.3) Thus, QFs are created for NASA’s proposed model on the basis of a number of empirical relationships and param - eters that have been judged to give a reasonable description of the RBEs for a variety of available radiobiological data for heavy ions. These available radiobiological data are from animal and cellular systems but are quite limited in scope and in their relationship to carcinogenesis, as described in the section above entitled “Cancer Risks and Radiation Quality in the Model.” The parametric forms of the QF relationships have been guided by aspects of track structure and by aspects of a specific biophysical model, but it is stated that the interpretation of the parameters is not tied to any particular model. These empirical relationships replace the Q(L) relationship used in the current NASA risk model. The committee considers NASA’s proposed definition of QF to be reasonable and agrees that the parametric forms should be regarded as essentially empirical and not as having been formally derived from biophysical first principles. Conclusion: In the proposed model, different maximum values of quality factor, QF, are assumed for leukemia (maximum 10) and for solid tumors (maximum 40). This is a change from the current NASA risk model. The committee agrees that it is reasonable to make such a distinction on the basis of the limited animal and human data available. The concepts underlying this new parameterization of QF and the rationale for their use are discussed in the section above entitled “Cancer Risks and Radiation Quality in the Model.” Overall, the committee considers that the new parameterization, although more complicated, provides an improvement over the previous LET descriptions, particularly for the wide diversity of HZE and lower-energy charged particles to which astronauts are exposed in space. Not only are the new QF relationships likely to be more accurate for the proposed risk model, but they are also more amenable to uncertainty analysis and can guide future research aimed at reducing the parameter uncertainties and improving the form of parameterization. At a purely empirical level, plotting the relative biological effectiveness of charged particles against Z*2/β2 does tend to bring the RBE data closer toward a single curve (for a given biological system) compared to the plot - ting of RBE against LET. However, as is noted in the 2011 NASA report, the uncertainties related to the quality factor are still the largest contributor to the overall uncertainty of REID. Model Improvements and Recommendations For NASA’s proposed model, values for the three parameters that define QF have been selected by comparison with experimentally observed variations in biological effectiveness with radiation type for some cellular biological effects and by considering the few available data on the induction of cancer by high-LET radiation. However, it is not clear from the 2011 NASA report how the particular values were decided, including the ad hoc selection of different values for parameters κ and Σ0/αγ for ions of Z ≤ 4 compared to all ions of higher charge, and what analyses were carried out.
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43 REVIEW OF NASA MODEL Recommendation: The committee recommends that NASA make a detailed comparison of the relative biological effectiveness versus Z*2/β2 dependence of the experimental data with the proposed form and parameters of the quality factor, QF, equation in order to improve the transparency of the basis for the selection of the proposed parameter values for the model and to provide guidance for future research to test, validate, modify, and/or extend the parameterization. This analysis needs to include the defined selection of different values for parameters κ and Σ0/αγ for ions of Z ≤ 4 compared to all ions of higher charge. The risk equation of NASA’s proposed model (see Equation 2.1 above) follows directly from the proposed risk cross-section Equation 5.19 of the 2011 NASA report (Cucinotta et al., 2011), namely: �γ��� ���� �� � �0���� �� � � �� � ���� ���. 6.24 (2.4) This equation partitions the risk from a single heavy ion into two components, one of which behaves exclu - sively as the low-LET component of energy deposition and the other exclusively as the high-LET component, with no radiobiological interaction between the two components. This important assumption is introduced in the 2011 NASA report as Equation 5.19 (Cucinotta et al., 2011), but without any clear attempt to justify it or to present a rationale for its use. The committee recommends that work be carried out to validate this assumption. Key Element: Effective Dose Model Completeness The ICRP defines the quantity “effective dose” for use in radiation protection and states that it is intended mainly for use in “prospective dose assessment for planning and optimisation in radiological protection, and demonstra - tion of compliance with dose limits for regulatory purposes” (ICRP, 2007, p. 13). Its computation includes tissue weighting factors (wT), the specific normalized values of which are defined by ICRP for the individual tissues of the body as an approximate gender-averaged representation of the relative contribution of each tissue to the radiation detriment of stochastic effects from uniform whole-body low-LET irradiation. The 2011 NASA report defines its own quantity, which it also calls effective dose, and it states that this quantity is strictly for internal NASA use. This NASA quantity is analogous to ICRP effective dose but is based on NASA’s own gender-specific sets of relative tissue weights (wT) for the space radiation environment and NASA’s defini- tion of quality factor for the space radiation environment. Values for wT are chosen in the 2011 NASA report to match the estimated tissue-specific components of REID from the various tissues in representative space radiation environments, including the high-LET radiation components. Values of wT are calculated by NASA’s proposed model. These NASA wT values can differ substantially from the ICRP values for a variety of reasons, particularly due to gender-specificity, inclusion of only radiation-induced death from cancer and not other forms of health detriment, the low penetration of some components of space radiation (such as the lower-energy SPE protons), and different QFs for leukemia compared to solid cancers. The committee considers NASA’s proposed model to be an appropriate tool for calculating this summary metric. It should be noted that the partly substantial changes in weighting factors that NASA uses for evaluating its effective dose in comparison to the ICRP effective dose do not lead to major differences under many space radiation environments, as illustrated in Figures 6.4 and 6.5 in the 2011 NASA report (Cucinotta et al., 2011). In circumstances in which there are large differences, such as from SPE exposures, it is explained in the report that these are due to the relatively much larger doses to superficial organs compared to the more cancer-prone deep- seated organs. It would be useful to make clear that the main driver of these differences in effective dose is the chest/breast, which alone is responsible for about 40 percent of the total ICRP effective dose in the case of the SPE
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44 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS example given in Table 2.7 of the 2011 NASA report (Cucinotta et al., 2011), due to the large superficial dose to this tissue and to its large ICRP tissue weighting factor (0.12). Model Improvements and Recommendations As described above, NASA proposes to use its own summary quantity for mission operational purposes, and in the 2011 NASA report this quantity is simply called effective dose. However, “effective dose” is, strictly speaking, a quantity defined by ICRP, including ICRP-defined specification of numerical values for weighting factors and gender-averaging. If different tissue weighting factors and radiation quality specifications are used and effective dose is evaluated without gender-averaging, it is problematical for the resulting quantity still to be called effective dose and the unit sievert given to its numerical values. The committee believes that the NASA description of the proposed model would be improved by the use of terminology and notation that distinguish NASA-defined quantities (especially effective dose) from those defined by ICRP. Wide dissemination of the definition of “effective dose” within NASA and the research community would reduce confusion about what exactly is being measured or limited. It is not clear from the 2011 NASA report in what ways the NASA-defined effective dose will be implemented as a summary variable for mission operations. The provision of further information would be useful, including what quantity or quantities are used for the calibration of area and personal monitors in space vehicles and how these calibration quantities compare with the NASA versions of effective dose. Key Element: Tissue-Specific Particle Spectra, F(E,Z) Model Completeness The assessment of cancer risk due to space radiation begins with defining the external (or ambient) radiation environments, which in turn are inputs to transport-shielding calculations to obtain the local radiation environ - ment, modified by spacecraft and body shielding, at tissues of concern. GCR and particles from SPE are two major components3 of the space radiation environment. Hence, tissue-specific particle fluence spectra, denoted by F(E,Z), are obtained as input to the cancer risk calculation, together with all of the components described above. The committee concludes that NASA’s proposed model uses standard and well-studied methods for the specifica - tion of the space radiation environment and computation of the tissue-specific particle spectra. Model Improvements and Recommendations The radiation environment and shielding transport models in NASA’s proposed model are considered by the committee to be a major step forward compared to previous models used (especially the introduction of the statistical SPE model to NASA’s proposed model in place of the current SPE model). The proposed SPE model is not actually a probabilistic risk assessment (PRA), as is suggested in the Executive Summary of the 2011 NASA report (Cucinotta et al., 2011), but rather a statistical model developed using a data set from past measurements. The committee agrees that the proposed radiation environment and shielding transport models have been developed with the extensive use of available data and rigorous mathematical analyses. The uncertainties conser- vatively allocated to the space physics parameters (i.e., environment and transport models) are deemed adequate at this time, considering that the space physics uncertainty is only a minor contributor to the overall cancer risk assessment. The currently used paradigm for both galactic cosmic rays and solar energetic particles is based entirely on the statistics obtained from past measurements. The committee agrees that the specification of the space physics parameters is done as well as it can be with this approach, and it may well be adequate for NASA’s purpose given other uncertainties in REID. However, the committee suggests that estimates could be improved by adding physics-based studies of particle transport using the current picture of the heliosphere and its electric and magnetic fields. Particle transport in the interplanetary medium is determined by the electric and magnetic fields 3As stated in footnote 1 at the beginning of this chapter, trapped-particle models are not covered here because they contribute very little to the organ dose for missions aboard the International Space Station or missions to the Moon or Mars.
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45 REVIEW OF NASA MODEL in the solar wind. Theoretical and numerical studies of particle trajectories should certainly result in improved transport models and smaller uncertainties in the environmental estimates. However, this would involve a major effort and change in modeling approach and may not be warranted in view of the relatively minor contribution of space physics uncertainties. Key Element: Uncertainties Model Completeness The committee considers that the handling and combining of uncertainties in the NASA’s proposed organ-site-specific models, as presented in Table 6.5 of the 2011 NASA report (Cucinotta et al., 2011), are logi - cal and appropriate as applied to statistical errors, bias in cancer incidence data, dosimetry errors, transfer model weights, and DDREF for low-LET radiation, and for radiation quality factors and risk cross sections for space radiation, as well as for the physics uncertainties of space radiation. It is not clear, however, that the use of empirical Bayes estimates for the calculation of cancer risks for mul - tiple organ sites, as discussed in Sections 3, 3.1, and 4.2 and in Table 4.1 of the 2011 NASA report, offers any advantage for estimating cancer mortality risk for all organ sites combined, as discussed in the following section. Model Improvements and Recommendations NASA’s proposed model does not include an upward bias correction for dosimetric uncertainty in the under- lying life span study data (Pierce et al., 2008); however, the correction would be small at the dose levels anticipated for astronauts in the near future. In the 2011 NASA report, the discussion of the use of a maximum likelihood estimate (MLE) and empirical Bayes (EB) estimate of site-specific ERR per sievert in Section 4.2 and Table 4.1 is somewhat confusing. For example, as shown in the table, the site-specific EB estimate of ERR per sievert for kidney cancer (0.40) would be similar to the MLE (also 0.40 for this particular organ site), with a lower estimated standard error (0.19) compared to the MLE standard error of 0.32. This difference is due to the fact that the site-specific EB estimate uses additional risk information from organ sites other than the kidney, and the MLE estimate does not. However, the variance (and therefore the standard error) of the summed site-specific estimates of ERR per sievert over all cancer site groupings in the table should be similar for the MLE and EB approaches, because the EB error calculation would include information from the (mostly positive) off-diagonal elements of the covariance matrix as well as the estimated variances. It is not clear whether the EB approach has been used in NASA’s proposed model. Recommendation: On the assumption that the empirical Bayes approach has been used in NASA’s proposed model, the committee recommends that the authors ensure that the off-diagonal covariance information has been taken into account. If the EB approach has not been used, either this fact should be stated in the text of the 2011 NASA report (Cucinotta et al., 2011) or the references to the EB approach should be removed from the text. The uncertainty analysis in NASA’s proposed model reveals that the value of QF is the largest contributor to the uncertainty of REID, alone introducing about 3.4-fold uncertainty in risk. Additionally, it is found that this component is reduced to 2.8-fold uncertainty if two of the track structure parameters are constrained to a fixed algebraic relationship to one another (such that the Z*2/β2-position of the maximum value of QF is held fixed). Conclusion: NASA’s proposed model discusses the observation that the use of a fixed relationship be - tween two track structure parameters reduces the uncertainty as being a potentially valuable finding that may provide a method to reduce uncertainty in estimations of the REID. However, little indication is given in the 2011 NASA report as to why such a fixed position might be justified or expected. The
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46 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS committee suggests that further investigations into the validity and usefulness of this approach would be worthwhile. Key Element: Other Issues Non-Cancer Effects (Tissue Reactions) In its proposed approach to estimating the safe days in deep space, NASA has used a 3 percent REID for fatal cancer as the limit. In its current (2005) model, NASA also considers dose limits for non-cancer effects—lens, skin, blood-forming organs, heart and central nervous system. For example, “career limits for the heart are intended to limit the REID for heart disease to be below approximately 3 to 5 percent, and are expected to be largely age and sex independent” (NASA, 2005, p. 65). It was further assumed by NASA that the limits established would restrict mortality values for these non-cancer effects to less than the risk level for cancer mortality. The cancer and non- cancer risks were not combined into a single REID. More recent data have led ICRP to reconsider the threshold dose values, particularly for the cardiovascular system (and cataracts) (see ICRP, 2011). It is concluded by ICRP (2011) that a threshold absorbed dose of 0.5 Gy should be considered for cardiovascular disease (and cataracts) for acute and for fractionated/protracted exposures. It is appreciated by ICRP that these values have a degree of uncertainty associated with them. For example, several reports suggest that effects on the cardiovascular system occur only at much higher dose levels (Mulrooney et al., 2009; Davis et al., 1989). Conclusion: The revised value for the threshold dose value proposed by ICRP suggests that NASA may need to consider how it might account for cardiovascular disease in their calculations of dose limits. However, it is noted that to date there is very little information on RBE for non-cancer effects that is needed for risk estimates for space radiation exposures. By continuing to monitor developments in the area of potential cardiovascular effects at low doses of radia - tion, NASA would have the opportunity to determine if there is a need to modify the proposed estimates of REID. Delayed Effects Delayed effects pertinent to the assessment of risk principally relate to observations whereby radiation-induced genomic instabilities have been reported, as measured by the appearance of a delayed increase in the rates of new chromosomal aberrations, mutations, or micronuclei in cells several cell generations after irradiation. Such effects could have important implications for radiation protection in view of current notions of the multistep mutational processes involved in carcinogenesis. An early induced change in subsequent and ongoing mutation rates in irradi - ated somatic cells could accelerate this process. Conclusion: There are conflicting reports on the generality of the phenomenon of radiation-induced delayed genomic instability and some question about variation in susceptibilities of cells from different individuals with regard to this effect. Thus, the committee concludes that it is appropriate that genomic instability not be incorporated into the model, in agreement with the proposed NASA approach. How - ever, the committee considers that further investigation of the phenomenon is certainly warranted. Non-Targeted Effects Non-targeted effects largely refer to the so-called bystander effects, by which responses can be produced in an unirradiated cell as a result of the transfer of a signal from an irradiated cell. For HZE radiations, doses that may be received by astronauts are very non-uniform in the sense that some cells will be traversed by the primary particle itself, whereas other cells will not be traversed; thus, an NTE is also a phenomenon that is of considerable interest.
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47 REVIEW OF NASA MODEL Conclusion: While the 2011 NASA report contains an extended discussion on non-targeted effects (NTEs) and their potential impact on risk estimates, they appropriately chose not to include these NTEs in their proposed model at this time. Little is known in qualitative or quantitative terms of the contribu- tion of these NTEs directly related to radiation-induced carcinogenesis, but the committee believes that studies to elucidate any such relevance should be encouraged. Qualitative Differences It is recognized that there are qualitative differences in the nature of the initial energy depositions and hence in initial chemical, biochemical, and biological damages from different types of ionizing radiation. Differences are particularly great between low-LET gamma rays and the wide variety of high-LET heavy ions in space radia - tion. This may lead to observed differences in responses of cells, tissues or organisms such as different spectra of mutations and chromosome aberrations, altered gene-expression patterns, and different types and latencies of cancer. There is experimental evidence for qualitative differences at each of the above levels of biological effect. As a result, it may not be entirely appropriate to apply universal QFs as quantitative scaling factors, based on quantities such as RBE that assume similar underlying biological processes. This is an area in which experiments quantifying types, frequencies, and latencies of various cancers—for example, lung, colon, and breast cancer—as well as the studies of leukemia and liver cancer mentioned above, are sorely needed for radiations of varying LET, especially at low particle fluences such as in space. Furthermore, the tumor studies would need to be coupled with appropriate mechanistic investigations to provide understanding of the underlying carcinogenic processes. Probabilistic Risk Assessment The performance of a probabilistic risk assessment of spaceflight cancer risk implies the consideration of a comprehensive set of radiation exposure scenarios involving an array of radiation hazards over a long period of time and an assessment of the vulnerability of a complex engineered system under a variety of threats. Although it was not the intent of NASA’s proposed model to be comprehensive in terms of the risk scenarios, as the focus was limited to the health effects component of a total system risk model, it is important to recognize the limitations of the model in reference to best practices in total-system PRA. Details were lacking in the 2011 NASA report regarding the PRA context of NASA’s current (2005) model and the planned steps toward an eventual total-system cancer risk model. Eventual movement to a total-system cancer risk model would require the development of scenario sets that include not only the quantification of the health effects but the details of the dynamics of the radiation source term and consideration of the “what can go wrong” scenarios associated with specific missions. Examples of such scenarios are unexpected solar particle events and a failure of radiation protection systems. The extent to which NASA’s current (2005) model accommodates multiple scenarios is not clear. Experience suggests that several exposure and shielding scenarios will have to be considered should the decision be made to perform comprehen - sive, mission-specific risk assessments. The 2011 NASA report (Cucinotta et al., 2011) does make clear that there are only two radiation sources of interest, GCR and SPE. The report includes data suggesting that the GCR radiation environment is well character - ized and nearly constant over time during solar-cycle activity minima. The report also includes data suggesting that SPEs may not contribute significantly to risk during solar-cycle activity minima and may be mitigated by shielding. This may be the case with respect to the radiation source term, although it is doubtful, but it is certainly not the case with respect to quantifying “what can go wrong” scenarios. It is possible that a single radiation hazard scenario is sufficient for a comprehensive PRA, but it would be a unique circumstance in the practice of PRA, and there is a lack of evidence to support such a condition. It is always possible that many “what can go wrong” scenarios can be recovered from during a mission, but if that is part of the process, then recovery activities, such as the possible need for extravehicular activity, also have to be evaluated for their contribution to the overall risk. Evidence was not presented to indicate that such scenarios were actually considered or, that if they were, how they entered into the risk assessment.
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48 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS A major strength of comprehensive PRA is the quantification of the uncertainties and the importance ranking of the contributors to risk. The absence of the importance ranking of the contributors, including their uncertain - ties, compromises the comprehensiveness and possibly the capability of NASA’s proposed model. To be sure, the NASA methodology for characterizing uncertainty in the health effects model is comprehensive, but the point is that there is much more to a comprehensive risk assessment of the cancer risk of a space mission than the health effects model. In order to perform total-system and comprehensive risk assessments, much more attention to detail is required, particularly with respect to quantifying other factors contributing to risk, such as the engineering driven “what can go wrong” scenarios that experience indicates to be major contributors to risk. REFERENCES Alpen, E.L., Powers-Risius, P., Curtis, S.B., and DeGuzman, R. 1993. Tumorigenic potential of high-Z, high-LET charged particle radiation. Radiation Research 88:132-143. Alpen, E.L., Powers-Risius, P., Curtis, S.B., DeGuzman, R., and Fry, R.M.J. 1994. Fluence-based relative biological effectiveness for charged particle carcinogenesis in mouse Harderian gland. Advances in Space Research 14:573-581. Anderson, R.M., Marsden, S.J., Wright, E.G., Kadhim, M.A., Goodhead, D.T., and Griffin, C.S. 2000. Complex chromosome aberrations in peripheral blood lymphocytes as a potential biomarker of exposure to high-LET alpha-particles. International Journal of Radiation Biology 76:31-42. Anderson, R.M., Tsepenko, V.V., Gasteva, G.N., Molokanov, A.A., Sevan’kaev, A.V., and Goodhead, D.T. 2005. mFISH analysis reveals com- plexity of chromosome aberrations in individuals occupationally exposed to internal plutonium: A pilot study to assess the relevance of complex aberrations as biomarkers of exposure to high-LET alpha particles. Radiation Research 163:26-35. Armstrong, B.G. 1990. The effects of measurement errors on relative risk regressions. American Journal of Epidemiology 132(6):1176-1184. Baltschukat, K., and Horneck, G. 1991. Response to accelerated heavy ions of spores of Bacillus subtilis of different repair capacity. Radiation and Environmental Biophysics 30:87-104. Barth, J. 1997. “Modeling Space Radiation Environments,” presentation at the IEEE Nuclear and Space Radiation Effects Conference Short Course: Applying Computer Simulation Tools to Radiation Effects Problems, July 21, 1997, IEEE NPSS Radiation Effects Committee. NASA Goddard Space Flight Center, Greenbelt, Md. Bedford, J.S., and Goodhead, D.T. 1989. Breakage of human interphase chromosomes by alpha particles and X-rays. International Journal of Radiation Biology 55:211-216. Belli, M., Goodhead, D.T., Ianzini, F., Simone, G., and Tabocchini, M.A. 1992. Direct comparisons of biological effectiveness of protons and alpha-particles of the same LET. II. Mutation induction at the HPRT locus in V79 cells. International Journal of Radiation Biology 61:625-629. Belli, M., Cera, F., Cherubini, R., Haque, A.M.I., Ianzini, F., Moschini, G., Sapora, O., Simone, G., Tabocchini, M.A., and Tiverton, P. 1993. Inactivation and mutation induction in V79 cells by low energy protons: Re-evaluation of the results at the LNL facility. International Journal of Radiation Biology 63:331-337. Best, T., Li, D., Skol, A.D., Kirchhoff, T., Jackson, S.A., Yasui, Y., Bhatia, S., Strong, L.C., Domchek, S.M., Nathanson, K.L., Olopade, O.I., et al. 2011. Variants at 6q21 implicate PRDM1 in the etiology of therapy-induced second malignancies after Hodgkin’s lymphoma. Natural Medicine 17:941-943. Boice, J.D. 1993. Leukemia risk in thorotrast patients. Radiation Research 136:301-302. Burns, F., Yin, Y., Koenig, K. L., and Hosselet, S. 1993. The low carcinogenicity of electron radiation relative to argon ions in rat skin. Radiation Research 135:178-188. Burns, F., Yin, Y., Garte, S.J., and Hosselet, S. 1994. Estimation of risk based on multiple events in radiation carcinogenesis of rat skin. Advances in Space Research 14:507-519. Cardis, E., Vrijheid, M., Blettner M., Gilbert, E., Hakama, M., Hill, C., Howe, G., Kaldor, J., Muirhead, C.R., Schubauer-Berigan, M., Yoshimura, et al. 2005. Risk of cancer after low doses of ionising radiation: Retrospective cohort study in 15 countries. British Medical Journal 331(7508):77. Costes, S.V., Boissière, A., Ravani, S., Romano, R., Parvin, B., and Barcellos-Hoff, M.H. 2006. Imaging features that discriminate between foci induced by high- and low-LET radiation in human fibroblasts. Radiation Research 165:505-515. Cox, R., Thacker, J., Goodhead, D.T., and Munson, R.J. 1977a. Mutation and inactivation of mammalian cells by various ionizing radiations. Nature 267:424-427. Cox, R., Thacker, J., Goodhead, D.T., and Munson, R.J. 1977b. Response of 2 different mammalian-cell types to high LET radiations. British Journal of Radiology 50:537-537. Cucinotta, F.A., and Durante, M. 2006. Cancer risk from exposure to galactic cosmic rays: Implications for space exploration by human beings. Lancet Oncology 7:431-435. Cucinotta, F.A., and Chappell, L.J. 2010. Non-targeted effects and the dose response for heavy ion tumor induction. Mutation Research 687:49-53. Cucinotta, F.A., Wilson, J.W., Shavers, M.R., and Katz, R. 1996. Effects of track structure and cell inactivation on the calculation of heavy ion mutation rates in mammalian cells. International Journal of Radiation Biology 69:593-600.
OCR for page 49
49 REVIEW OF NASA MODEL Cucinotta, F.A., Wilson, J.W., Shavers, M.R., and Katz, R. 1997. The Calculation of Heavy Ion Inactivation and Mutation Rates in the Track Structure Model. NASA TP-1997-3630. NASA Johnson Space Center, Houston, Tex. Cucinotta, F.A., Nikjoo, H., and Goodhead, D.T. 2000. Model of the radial distribution of energy imparted in nanometer volumes from HZE particles. Radiation Research 153:459-468. Cucinotta, F.A., Kim, M.-H.Y., and Chappell, L.J. 2011. Space Radiation Cancer Risk Projections and Uncertainties—2010. NASA/TP-2011- 216155. NASA Johnson Space Center, Houston, Tex. July. Cullings, H.M., Fujita, S., Funamoto, S., Grant, E.J., Kerr, G.D., and Preston, D.L. 2006. Dose estimation for atomic bomb survivor studies: Its evolution and present status. Radiation Research 166:219-254. Davis, F.G., Boice, J.D., Jr., Hrubec, Z., and Monson, R.R. 1989. Cancer mortality in a radiation-exposed cohort of Massachusetts tuberculosis patients. Cancer Research 49(21):6130-6136. Dicello, J.F., Christian, A., Cucinotta, F.A., Gridley, D.S., Kathirithamby, R., Mann, J., Markham, A.R., Moyers, M.F., Novak, G.R., Piantadosi, S., Ricart-Arbona, R., et al. 2004. In vivo mammary tumorigenesis in the Sprague-Dawley rat and microdosimetric correlates. Physics in Medicine and Biology 49:3817-3830. Ding, L.-H., Shingyoji, M., Chen, F., Hwang, J.-J., Burma, S., Lee, C., Cheng, J.-F., and Chen, D.J. 2005. Gene expression profiles of normal human fibroblasts after exposure to ionizing radiation: A comparative study of low and high doses. Radiation Research 164:17-26. Doll, R., Peto, R., Wheatley, K., Gray, R., and Sutherland, I. 1994. Mortality in relation to smoking: 40 years’ observations on male British doctors. British Medical Journal 309(6959):901-911. Dugan, L.C., and Bedford, J.S. 2003. Are chromosomal instabilities induced by exposure of cultured normal human cells to low- or high-LET radiation? Radiation Research 159:301-311. Elkind, M.M., and Whitmore, G.F. 1967. The Radiobiology of Cultured Mammalian Cells. Gordon and Breach, New York. EPA (Environmental Protection Agency). 1999. Estimating Radiogenic Cancer Risks. Report 402-R-00-003. Washington, D.C. EPA. 2011. Radiogenic Cancer Risk Models and Projections for the U.S. Population. EPA 402-R-11-001. Washington, D.C. Feynman, J., and Gabriel, S., eds. 1988. Interplanetary Particle Environment: Proceedings of Conference. NASA-CR-185461. JPL Publication 88-28. Jet Propulsion Laboratory, Pasadena, Calif. Feynman, J., Armstrong, T., Dao-Gibner, L., and Silverman, S. 1990. New interplanetary proton fluence model. Journal of Spacecraft and Rockets 27:403-410. Feynman, J., Spitale, G., and Wang, J. 1993. Interplanetary proton fluence model. Journal of Geophysical Research 98:13281-13294. Feynman, J., Ruzmaikin, A., and Berdichevsky, V. 2002. The JPL proton fluence model: An update. Journal of Atmospheric and Solar-Terres- trial Physics 64:1679-1686. Flint-Richter, P., and Sadetzki, S. 2007. Genetic predisposition for the development of radiation-associated meningioma: An epidemiological study. Lancet Oncology 8:403-410. Fournier, C., Barberet, P., Pouthier, T., Ritter, S., Fischer, B., Voss, K.O., Funayama, T., Hamada, N., Kobayashi, Y., and Taucher-Scholz, G. 2009. No evidence for DNA and early cytogenetic damage in bystander cells after heavy-ion microirradiation at two facilities. Radiation Research 171:530-540. Fry, R.J.M., Powers-Risius, P., Alpen, E.L., and Ainsworth, E.J. 1985. High LET radiation carcinogenesis. Radiation Research 104:S188-S195. Furukawa, K., Preston, D.L., Lönn, S., Funamoto, S., Yonehara, S., Matsuo, T., Egawa, H., Tokuoka, S., Ozasa, K., Kasagi, F., Kodama, K., and Mabuchi, K. 2010. Radiation and smoking effects on lung cancer incidence among atomic bomb survivors. Radiation Research 174(1):72-82. George, K.A., and Cucinotta, F.A. 2007. The influence of shielding on the biological effectiveness of accelerated particles for the induction of chromosome damage. Advances in Space Research 39:1076-1081. Gilbert, E.S. 1984. Some effects of random dose measurement errors on analyses of atomic bomb survivor data. Radiation Research 98:591-605. Goodhead, D.T. 1989. Relationship of radiation track structure to biological effect: A re-interpretation of the parameters of the Katz model. Nuclear Tracks and Radiation Measurements 116:177-184. Goodhead, D.T., and Nikjoo, H. 1989. Track structure analysis of ultrasoft X-rays compared to high- and low-LET radiations. International Journal of Radiation Biology 55:513-529. Goodhead, D.T., Thacker, J., and Cox, R. 1979. Effectiveness of 0.3 keV carbon ultrasoft X-rays for the inactivation and mutation of cultured mammalian-cells. International Journal of Radiation Biology 36:101-115. Goodhead, D.T., Munson, R.J., Thacker, J., and Cox, R. 1980. Mutation and inactivation of cultured mammalian cells exposed to beams of accelerated heavy ions. IV. Biophysical interpretation. International Journal of Radiation Biology 37:135-167. Goodwin, E.H., Bailey, S.M., Chen, D.J., and Cornforth, M.N. 1996. The effect of track structure on cell inactivation and chromosome damage at a constant LET of 120 keV/micrometer. Advances in Space Research 18:93-98. Grogan, H.A., Sinclair, W.K., and Voilleque, P.G. 2001. Risks of fatal cancer from inhalation of 239,240 plutonium by humans: A combined four-method approach with uncertainty evaluation. Health Physics 80:447-461. Hande, M.P., Azizova, T.V., Geard, C.R., Burak, L.E., Mitchell, C.R., Khokhryakov, V.F., Vasilenko, E.K., and Brenner, D.J. 2003. Past exposure to densely ionizing radiation leaves a unique permanent signature in the genome. American Journal of Human Genetics 72:1162-1170. Held, K.D. 2009. Effects of low fluences of radiations found in space on cellular systems. International Journal of Radiation Biology 85:379-390. Hofer, E. 2007. Hypothesis testing, statistical power, and confidence limits in the presence of epistemic uncertainty. Health Physics 92(3):226-235. Hofer, E. 2008. How to account for uncertainty due to measurement errors in an uncertainty analysis using Monte Carlo simulation. Health Physics 95(3):277-290.
OCR for page 50
50 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS ICRP (International Commission on Radiological Protection). 1991. 1990 Recommendations of the ICRP. ICRP Publication 60. Pergamon Press, Elsevier Science, Oxford, U.K. ICRP. 2006. Low-Dose Extrapolation of Radiation-Related Cancer Risk. ICRP Publication 99. ICRP. Pergamon Press, Elsevier Science, Oxford, U.K. ICRP. 2007. The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Pergamon Press, Elsevier Science, Oxford, U.K. ICRP. 2011. Early and Late Effects of Radiation in Normal Tissues and Organs: Threshold Doses for Tissue Reactions in a Radiation Protec- tion Context. Draft Report for Consultation. ICRP Ref 4844-6029-7736. International Commission on Radiological Protection, Ottawa, Ontario, Canada. January 20. Jacob, P., Rühm, W., Walsh, L., Blettner, M., Hammer, G., and Zeeb, H. 2009. Is cancer risk of radiation workers larger than expected? Occu- pational and Environmental Medicine 66(12):789-796. Jokipii, J.R. 2010. The heliosphere and cosmic rays. Pp. in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.). Cambridge University Press, New York, 2010. Kadhim, M.A. 2003. Role of genetic background in induced instability. Oncogene 22:6994-6999. Kiefer, J., Stoll, U., and Schneider, E. 1994. Mutation induction by heavy ions. Advances in Space Research 14:257-265. Kim, M.-H.Y., Wilson, J.W., Cucinotta, F.A., Simonsen, L.C., Atwell, W., Badari, F.F., and Miller, J. 1999. Contribution of High Charge and Energy (HZE) Ions During Solar-Particle Events of September 29, 1989. NASA/TP-1999-209320. NASA, Washington, D.C. Kim, M.Y., Hayat, M.J., Feiveson, A.H., and Cucinotta, F.A. 2009. Prediction of frequency and exposure level of solar particle events. Health Physics 97:68-81. Kim, M.Y., Angelis, G.D., and Cucinotta, F.A. 2011. Probabilistic assessment of radiation risk for astronauts in space mission. Acta Astronautica 68:747-759. King, J.H. 1974. Solar proton fluences for 1977-1983 space missions. Journal of Spacecraft 11:401-408. Kocher, D., Apostoaei, A.I., Henshaw, R.W., Hoffman, F.O., Schubauer-Berigan, M.K., Stancescu, D.O., Thomas, B.A., Trabalka, J.R., Gilbert, E.S., and Land, C.E. 2008. Interactive Radioepidemiological Program (IREP): A web-based tool for estimating probability of causation/ assigned share for radiogenic cancers. Health Physics 95(1):119-147. Kodama, Y., Ohtaki, K., Nakano, M., Hamasaki, K., Awa, A.A., Lagarde, F., and Nakamura, N. 2005. Clonally expanded T-cell populations in atomic bomb survivors do not show excess levels of chromosome instability. Radiation Research 164:618-626. Kranert, T., Schneider, E., and Kiefer, J. 1990. Mutation induction in V79 Chinese hamster cells by very heavy ions. International Journal of Radiation Biology 58:975-987. Little, M.P., Hoel, D.G., Molitor, J., Boice, J.D., Jr., Wakeford, R., and Muirhead, C.R. 2008. New models for evaluation of radiation-induced lifetime cancer risk and its uncertainty employed in the UNSCEAR 2006 report. Radiation Research 169:660-676. Lloyd, D.C., and Edwards, A.A. 1983. Chromosome aberrations in human lymphocytes: Effect of radiation quality, dose, and dose rate. Radiation Research, 98(3):23-49. Loucas, B.D., and Cornforth, M.N. 2001. Complex chromosome exchanges induced by gamma rays in human lymphocytes: An mFISH study. Radiation Research 155:660-671. Loucas, B.D., Eberle, R.L., Durante, M., and Cornforth, M.N. 2004. Complex chromatid-isochromatid exchanges following irradiation with heavy ions? Cytogenetic Genome Research 104:206-210. Lubin, J.H., Schafer, D.W., Ron, E., Stovall, M., and Carroll, R.J. 2004. A reanalysis of thyroid neoplasms in the Israeli Tinea capitis study accounting for dose uncertainties. Radiation Research 161:359-368. McCracken, K.G., Dreschhoff, G.A.M., Zeller, E.J., Smart, D.F., and Shea, M.A. 2001. Solar cosmic ray events for the period 1561-1994. 1. Identification in polar ice, 1561-1950. Journal of Geophysical Research 106:21585-21598. Mewaldt, R.A., Cohen, C.M.S., Labrador, A.W., Leske, R.A., Mason, G.M., Desai, M.I., Looper, M.D., Mazur, J.E., Selesnick, R.S., and Haggerty, D.K. 2005. Proton, helium, and electron spectra during the large solar particle events of October-November 2003. Journal of Geophysical Research 110:A09S18. Mezentsev, A., and Amundson, S.A. 2011. Global gene expression responses to low- or high-dose radiation in a human three-dimensional tissue model. Radiation Research 175:677-688. Muirhead, C.R., O’Hagan, J.A., Haylock, R.G., Phillipson, M.A., Willcock, T., Berridge, G.L., and Zhang, W. 2009. Mortality and cancer incidence following occupational radiation exposure: Third analysis of the National Registry for Radiation Workers. British Journal of Cancer 100(1):206-212. Mulrooney, D.A., Yeazel, M.W., Kawashima, T., Mertens, A.C., Mitby, P., Stovall, M., Donaldson, S.S., Green, D.M., Sklar, C.A., Robison, L.L., and Leisenring, W.M. 2009. Cardiac outcomes in a cohort of adult survivors of childhood and adolescent cancer: Retrospective analysis of the Childhood Cancer Survivor Study cohort. British Medical Journal 339:B4606. NASA. 2005. NASA Space Flight Human System Standard. Volume 1: Crew Health. NASA Technical Standard NASA-STD-3001 (Approved 03-05-2007). NASA, Washington, D.C. NCRP (National Council on Radiation Protection and Measurements). 1989. Guidance on Radiation Received in Space Activities. Report No. 98. Bethesda, Md. NCRP. 1997. Uncertainties in Fatal Cancer Risk Estimates Used in Radiation Protection. Report No. 126. Bethesda, Md. NCRP. 2000. Radiation Protection Guidance for Activities in Low-Earth Orbit. Report No. 132. Bethesda, Md. NCRP. 2001. Fluence-based Microdosimetric Event-based Methods for Radiation Protection in Space. Report No. 137. Bethesda, Md. NCRP. 2002. Operational Radiation Safety Program for Astronauts in Low-Earth Orbit: A Basic Framework. NCRP Report No. 142. Bethesda, Md.
OCR for page 51
51 REVIEW OF NASA MODEL NCRP. 2006. Information Needed to Make Radiation Protection Recommendations for Space Missions Beyond Low-Earth Orbit. NCRP Report No. 153. Bethesda, Md. NIH (National Institutes of Health). 1985. Report of the National Institutes of Health Ad Hoc Working Group to Develop Radioepidemiological Tables. National Institutes of Health, Bethesda, Md. NIH. 2003. Report of the NCI-CDC Working Group to Revise the 1985 NIH Radioepidemiological Tables. NIH Publication No. 03-5387. Bethesda, Md. NRC (National Research Council). 2006. Health Risks from Exposure to Low Levels of Ionizing Radiation, BEIR VII Phase 2. The National Academies Press, Washington, D.C. Nymmick, R.A., Panasyuk, M.I., and Suslov, A.A. 1996. Galactic cosmic ray flux simulation and prediction. Advances in Space Research 17(2):19-30. O’Neill, P.M. 2006. Badhwar-O’Neill galactic cosmic ray model update based on advanced composition explorer (ACE) energy spectra from 1997 to present. Advances in Space Research 37:1727-1733. Parzen, E.J. 1967. Stochastic Processes. Holden-Day, Inc., San Francisco, Calif. Pawel, D., Preston, D., Pierce, D., and Cologne, J. 2008. Improved estimates of cancer site-specific risks for A-bomb survivors. Radiation Research 169:87-98. Pierce, D.A., Stram, D.O., and Vaeth, M. 1990. Allowing for random errors in radiation dose estimates for the atomic bomb survivor data. Radiation Research 123(3):275-284. Pierce, D.A., Sharp, G.B., and Mabuchi, K. 2003. Joint effects of radiation and smoking on lung cancer risk among atomic bomb survivors. Radiation Research 159(4):511-520. Pierce, D.A., Vaeth, M., and Cologne, J.B. 2008. Allowance for random dose estimation errors in atomic bomb survivor studies: A revision. Radiation Research 170:118-126. Plante, I., and Cucinotta, F.A. 2008. Ionization and excitation cross sections for the interaction of HZE particles in liquid water and application to Monte-Carlo simulation of radiation tracks. New Journal of Physics 10:125020. Portess, D.I., Bauer, G., Hill, M.A., and O’Neill, P. 2007. Low dose irradiation of non-transformed cells stimulates the selective removal of pre- cancerous cells via intercellular induction of apoptosis. Cancer Research 63:1246-1253. Preston, D.L., Kusumi, S., Tomonaga, M., Izumi, S., Ron, E., Kuramoto, A., Kamada, N., Dohy, H., Matsuo, T., Nonaka, H., Thompson, D.E., Soda, M., and Mabuchi, K. 1994. Cancer incidence in atomic bomb survivors. Part III. Leukemia, lymphoma and multiple myeloma, 1950-1987. Radiation Research 137:S68-S97. Preston, D.L., Mattsson, A., Holmberg, E., Shore, R., Hildreth, N.G., Boice, Jr., J.D. 2002. Radiation effects on breast cancer risk: A pooled analysis of eight cohorts. Radiation Research, 158(2):220-235. Preston, D.L., Shimizu, Y., Pierce, D.A., Suyama, A., and Mabuchi, K. 2003. Studies of the mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950-1997. Radiation Research 160(4):381-407. Preston, D.L., Pierce, D.A., Shimizu, Y., Cullings, H.M., Fujita, S., Funamoto, S., and Kodama, K. 2004. Effect of recent changes in atomic bomb survivor dosimetry on cancer mortality risk estimates. Radiation Research 162:377-389. Preston, D.L., Ron, E., Tokuoka, S., Funamoto, S., Nishi N., Soda, M., Mabuchi, K., and Kodama, K. 2007. Solid cancer incidence in atomic bomb survivors: 1958-1998. Radiation Research 168:1-64. Prise, K.M., Schettino, G., Folkard, M., and Held, K.D. 2005. New insights on cell death from radiation exposure. Lancet Oncology 6:520-528. Ron, E., Carter, R., Jablon, S., and Mabuchi, K. 1994a. Agreement between death certificate and autopsy diagnoses among atomic bomb survivors. Epidemiology 5:48-58. Ron, E., Preston, D.L., Mabuchi, K., Thompson, D.E., and Soda, M. 1994b. Cancer incidence in atomic bomb survivors. Part IV: Comparison of cancer incidence and mortality. Radiation Research 137:S98-S112. Ron, E., Lubin, J.H., Shore, R.E., Mabuchi, K., Modan, B., Pottern, L., Schneider, A.B., Tucker, M.A., and Boice, J.D. 1995. Thyroid cancer after exposure to external radiation: A pooled analysis of seven studies. Radiation Research 141:259-277. Ruzmaikin, A., Feynman, J., and Jun, I. 2011a. Distribution of extreme solar energetic proton fluxes. Journal of Atmospheric and Solar- Terrestrial Physics 73:300-307. Ruzmaikin, A., Feynman, J., and Stoev, S.A. 2011b. Distribution and clustering of fast coronal mass ejections. Journal of Geophysics Research 116:A04220. Schafer, M., Schmitz, C., and Bucker, H. 1994. DNA double strand breaks induced in Escherichia coli cells by radiations of different quality. Radiation Protection Dosimetry 52:233-236. Schafer, D.W., and Gilbert, E.S. 2006. Some statistical implications of dose uncertainty in radiation dose-response analyses. Radiation Research 166(1 Pt 2):303-312. Schneider, A.B., Ron, E., Lubin, J., Stovall, M., and Gierlowski, T.C. 1993. Dose-response relationships for radiation-related thyroid cancer and thyroid nodules: Evidence for the prolonged effects of radiation on the thyroid. Journal of Clinical Endocrinology and Metabolism 77:362-364. Schollnberger, H., Mitchel, R.E.J., Redpath, J.L., Crawford-Brown, D.J., and Hofmann, H. 2007. Detrimental and protective bystander effects: A model approach. Radiation Research, 168:614-626. SEER (Surveillance Epidemiology and End-Result) Program of the National Cancer Institute. Version 6.4.4. 2011. Available at http://seer. cancer.gov/seerstat. Shea, M.A., and Smart, D.A. 1990. A summary of major solar proton events. Solar Physics 127:297-320. Shea, M.A., and Smart, D.F. 1995. History of solar proton event observations, Nuclear Physics B (Proc. Suppl.) 39A:16-25.
OCR for page 52
52 TECHNICAL EVALUATION OF THE NASA MODEL FOR CANCER RISK TO ASTRONAUTS Shilnikova, N.S., Preston, D.L., Ron, E., Gilbert, E.S., Vassilenko, E.K., Romanov, S.A., Kuznetsova, I.S., Sokolnikov, M.E., Okatenko, P.V., Kreslov, V.V., and Koshurnikova, N.A. 2003. Cancer mortality risk among workers at the Mayak nuclear complex. Radiation Research 159(6):787-798. Shore, R.E. 1992. Issues and epidemiological evidence regarding radiation-induced thyroid cancer. Radiation Research 131:98-111. Shore, R.E., Woodward, E., Hildreth, N., Dvoretsky, P., Hempelmann, L., and Pasternack, B. 1985. Thyroid tumors following thymus irradiation. Journal of the National Cancer Institute 74:1177-1184. Slaba, T.C., Blattnig, S.R., and Badavi, F.F. 2010a. Faster and more accurate transport procedures for HZETRN. Journal of Computational Physics 229:9397-9417. Slaba, T.C., Blattnig, S.R., Aghara, S.K., Townsend, L.W., Handler, T., Gabriel, T.A., Pinsky, L.S., and Redell, B. 2010b. Coupled neutron transport for HZETRN. Radiation Measurements 45:173-182. Thacker, J., Cox, R., and Goodhead, D.T. 1980. Do carbon ultrasoft X-rays induce exchange aberrations in cultured mammalian-cells? Interna- tional Journal of Radiation Biology 38:469-472. Thacker, J., Stretch, S., and Stephens, M.A. 1979. Mutation and inactivation of cultured mammalian cells exposed to beams of accelerate heavy ions. II. Chinese hamster V79 cells. International Journal of Biological Sciences 38:137-148. Thompson, D.E., Mabuchi, K., Ron, E., Soda, M., Tokunaga, M., Ochikubo, S., Sugimoto, S., Ikeda, T., Terasaki, M., Izumi, S., and Preston, D.L. 1994. Cancer incidence in atomic bomb survivors. Part II: Solid tumors, 1958-1987. Radiation Research 137:S17-S67. Thun, M.J., Hannan, L.M., Adams-Campbell, L.L., Boffetta, P., Buring, J.E., Feskanich, D., Flanders, W.D., Jee, S.H., Katanoda, K., Kolonel, L.N., Lee, I.M., et al. 2008. Lung cancer occurrence in never-smokers: An analysis of 13 cohorts and 22 cancer registry studies. PLoS Medicine 5(9):E185. Tylka, A.J., Adams, J.H., Boberg, P.R., Brownstein, B., Dietrich, W.F., Flueckiger, E.O., Petersen, E.L., Shea, M.A., Smart, D.F., and Smith, E.C. 1997. CREME96: A revision to cosmic ray effects on microelectronics code. IEEE Transactions on Nuclear Science 44:2150-2160. UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation). 2006. Studies of Radiation and Cancer. Report to the General Assembly, with Scientific Annexes A and B. United Nations, New York. UNSCEAR. 2008. Epidemiological Studies of Radiation and Cancer. Volume I, Scientific Annex A. United Nations, New York. Wakeford, R., Antell, B.A., and Leigh, W.J. 1998. A review of probability of causation and its use in a compensation scheme for nuclear industry workers in the United Kingdom. Health Physics 74(1):1-9. Weil, M.M., Bedford, J.S., Bielefledt-Ohmann, H., Ray, A.F., Gernick, P.C., Ehrhart, E.J., Falgren, C.M., Hailu, F., Battaglia, C.L.R., Charles, C., Callan, M.A., and Ullrich, R.L. 2009. Incidence of acute myeloid leukemia and hepatocellular carcinoma in mice irradiated with 1 GeV/nucleon 56Fe ions. Radiation Research 172:213-219. Whitehouse, C.A., and Tawn, E.J. 2001. No evidence for chromosomal instability in radiation workers with in vivo exposure to plutonium. Radiation Research 156:467-475. Xapsos, M.A., Stauffer, C., Gee, G.B., Barth, J.L., Stassinopoulos, E.G., and McGuire, R.E. 2004. Model for solar proton risk assessment. IEEE Transactions on Nuclear Science 51:3394-3398. Zhu, L.X., Waldren, C.A., Vannias, D., and Hei, T.K. 1996. Cellular and molecular analysis of mutagenesis induced by charged particles of defined linear energy transfer. Radiation Research 145(3):251-259.