NASA/TM-2012-217357

Probability of Causation for Space Radiation 

Carcinogenesis following International Space 

Station, Near Earth Asteroid, and Mars Missions

Francis A. Cucinotta

NASA Lyndon B. Johnson Space Center

Houston, Texas

Myung-Hee Y. Kim and Lori J. Chappell

U.S.R.A., Division of Space Life Sciences

Houston, Texas

February 2012
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NASA/TM-2012-217357

Probability of Causation for Space Radiation 

Carcinogenesis following International Space 

Station, Near Earth Asteroid, and Mars Missions 

Francis A. Cucinotta

NASA Lyndon B. Johnson Space Center

Houston, Texas

Myung-Hee Y. Kim and Lori J. Chappell

U.S.R.A., Division of Space Life Sciences

Houston, Texas

February 2012
Available from:

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7121 Standard Drive 5285 Port Royal Road

Hanover, MD 21076-1320 Springfield, VA 22161

This report is also available in electronic form at http://ston.jsc.nasa.gov/collections/TRS
v

Table of Contents

Glossary ................................................................................................................................... vi

Abstract.................................................................................................................................... viii

1. Introduction ....................................................................................................................... 1

2. Cancer Risk Projection Model........................................................................................... 4

3. Space Radiation and Organ Exposures.............................................................................. 7

4. Uncertainty Analysis......................................................................................................... 13 

5. Results............................................................................................................................... 17

6. Discussion and Conclusions.............................................................................................. 22

7. References......................................................................................................................... 24
vi

Glossary

ALARA as low as reasonably achievable

AR Attributable Risk

BEIR (NAS) Committee on the Biological Effects of Ionizing Radiation

BMI Body Mass Index

BRYNTRN baryon transport computer code

CDC Center for Disease Control

CI confidence interval

CL confidence level

CNS central nervous system

DDREF dose and dose-rate reduction effectiveness factor

Dr dose-rate (Gy/hr)

EAR excess additive risk, Sv–1

EB Empirical Bayes

ERR excess relative risk, Sv–1yr–1

EVA extravehicular activity

F fluence (number of ions per unit area) no. ions/cm2

FS former smokers

GCR galactic cosmic rays

GM geometric mean

GSD geometric standard deviation

HZE high-energy and charge

HZETRN high charge and energy transport computer code

ICRP International Commission on Radiological Protection

ISS International Space Station

LEO low Earth orbit

LET linear energy transfer, keV/m

LSS Life-Span Study of the Japanese atomic-bomb survivors

MLE Maximum Likelihood Estimates

NAS National Academy of Sciences

NEA Near Earth Asteroid

NRC National Research Council

NS Never-smoker (lifetime use less than 100 cigarettes)

NSRL NASA Space Radiation Laboratory

NTE Non-Targeted Effect

PC Probability of Causation

PDF probability distribution function
vii 

Q quality factor 

Q(L) quality factor as a function of linear energy transfer 

Qleukemia quality factor for estimating leukemia risks 

Qsolid quality factor for estimating solid cancer risks 

QMSFRG quantum multiple scattering fragmentation model 

RBE relative biological effectiveness 

RBEmax maximum relative biological effectiveness that assumes linear responses at 

low doses or dose-rates 

REIC risk of exposure-induced cancer incidence 

REID risk of exposure-induced death 

RERF Radiation Effects Research Foundation 

RR Relative Risks 

S smokers 

SD standard deviation 

SEER surveillance, epidemiology, and end results 

SMR Standard Mortality Ratio 

SPE solar particle event 

wT Tissue weighting factor 

 

xTr track structure scaling parameter equivalent to Z*

/2

xα quantiles (random variables) associated with factor α

UNSCEAR United Nations Special Committee on the Effects of Atomic Radiation 

 

Z Charge number 

Z* Effective charge number 

 Coefficient of linear dose response term, Gy-1

 particle velocity relative to the speed of light

j(x,E) number of particles of type j with energy, E at depth, x in shielding, 1/(MeV/u 

cm2

)

I gender and age-specific cancer incidence rate, cancers/yr 

 Parameter in action cross section to determine most biologically effects Z*2/2

 Track structure derived risk cross section, m2 
viii

Abstract

Cancer risk is an important concern for International Space Station (ISS) missions and future 

exploration missions to Mars and other destinations. As space programs mature, an important

question arises as to the likelihood of a causal association between a crew members’ radiation 

exposure and the occurrence of cancer. The probability of causation (PC), also denoted as 

attributable risk, is used to make such an estimate. PC estimates above 50% suggest an 

observed cancer was more likely to be attributed to radiation exposure than not. Because of the 

uncertainties in estimating radiation cancer risks, analysis of terrestrial occupational exposure

risk has estimated PC at the 95th or 99th percentile confidence level (CL) in assessing the 

possible relationship between prior radiation exposures and cancer. In this report, we first 

summarize the NASA model of space radiation cancer risks and uncertainties, including 

improvements to represent uncertainties in tissue-specific cancer incidence models for neversmokers (NS) and the U.S. average population. We then report on tissue-specific cancer 

incidence estimates and PC for different post-mission times for ISS and exploration missions.

Results show that leukemia and stomach cancer are most likely related to space radiation 

exposures followed by lung, colon, bladder, and liver cancers. PC estimates for a single ISS 

mission are not found to exceed 50% even at the 95% CL. However, median PC and 95% CL 

are found to exceed 50% for many cancer types for deep space missions. PC estimates for NS 

are estimated to be increased compared to a U.S. average population for lung and several other 

tissues, although absolute risks are reduced for NS compared to the U.S. average population. 

An important conclusion from our analysis is that the NASA policy to limit the risk of exposureinduced death to 3% at the 95% CL largely ensures that estimates of the PC for most cancer 

types would not reach a level of significance. Reducing uncertainties through radiobiological 

research remains the most efficient method to extend mission length and establish effective

mitigators for cancer risks. Efforts to establish biomarkers of space radiation-induced tumors 

and to estimate PC for rarer tumor types are briefly discussed.
1

1. Introduction

In this paper, we discuss methods to estimate the probability of causation (PC), also known as 

Attributable Risk (AR), for space radiation exposures. Astronauts are exposed to galactic cosmic 

rays (GCR)—made up of high-energy protons and high-energy and charge (HZE) nuclei, and 

solar particle events (SPEs)—comprised largely of low- to medium-energy protons, which are a 

critical challenge for space exploration. Experimental studies have shown that HZE nuclei produce 

both qualitative and quantitative differences in biological effects compared to terrestrial radiation 

(reviewed in NAS, 1996; Cucinotta and Durante, 2006; Durante and Cucinotta, 2008; 

Schimmerling et al., 1999; NCRP, 2006) leading to large uncertainties in predicting exposure 

health outcomes to humans. The uncertainties in estimating GCR health risks are a major 

limitation to the length of space missions and the evaluation of potential risk mitigators. NASA 

limits astronaut exposures to a 3% risk of exposure-induced death (REID), and protects against 

uncertainties in risks projections using an assessment of 95% confidence intervals (CIs) of risk 

estimates (Cucinotta et al., 2011). Beyond efforts related to risk limitation prior to a mission, there 

will also be a concern for cancers observed in crew members at post-mission and their possible 

association with prior space radiation exposures. The PC is a conditional probability of risk used 

as an indicator of a potential causal relationship between radiation exposure and occurrence of 

cancer in a population. The calculation of PC with concomitant uncertainty analysis will allow 

NASA to make estimates that an observed cancer was caused by occupational exposure;

however, the estimates should be augmented with considerations of an individual’s family history 

of disease, possible exposure to other carcinogens, and of individual based biomarkers. 

Astronauts and other healthy workers enjoy many lifestyle factors that lead to reduced lifetime 

cancer risks compared to the U.S. average population (Calle et al., 1999). Healthy worker 

attributes found for astronauts include optimal ranges of body mass index (BMI), moderate alcohol 

use, excellent nutrition and exercise regimes, and health care (LSAH 2003). More importantly, 

more than 90% of astronauts are never-smokers (NS) and therefore are expected to have lower 

background cancer rates than the U.S. average rates, which include current and former smokers 

along with NS. It is well known that NS have lower rates of cancer, circulatory and pulmonary 

diseases, and longer lifespan than former or current smokers (Thun et al., 1995; Doll et al., 2004). 

Indeed, more than 20% of all deaths in the U.S. are associated with tobacco exposure, including 

over 80% of all lung cancer deaths (CDC 2010). In addition, epidemiology studies suggest a 

harmful synergistic interaction between radiation and tobacco exposure occurs (Gilbert et al., 

2003; Furukawa et al., 2010; Leuraud et al., 2011; Cucinotta et al., 2012). Exposure to secondhand smoke would be variable in the astronaut or other healthy populations, and can significantly 

increase lung cancer and circulatory disease risk (CDC 2010). Radiation risk estimates have used 

models based on the U.S. average population (NCRP, 2000) until recently (Cucinotta and 

Chappell, 2011). Because cancer risk estimates are made using a mixture of multiplicative and 

additive risk transfer models, the lower background cancer rates of a healthy population reduce

radiation risk estimates compared to estimates for the U.S. average population (Cucinotta et al. 

2011, 2012). In this report, we show that the opposite effect will occur for the conditional 

probability represented by the PC, whereby the PC is increased for several radiogenic cancers for 

a NS population compared to the U.S. average population. 
2

Evidence that astronauts should be considered to be at lower risk for cancers and enjoy longer 

lifespan compared to the U.S. average population is borne out by analysis of Kaplan-Meir survival 

curves (Figure 1) and Standard Mortality Ratios (SMR) (Table 1), where the cohort of NASA 

astronauts and payload specialists is compared to the U.S. average population (CDC MMWR 

2008) and the estimates for a NS population. These comparisons include results after censoring 

18 of 19 occupationally related accidental deaths from space missions or training considered 

atypical of U.S. workers. The largely male cohort of astronauts and payload specialists show a 

longer longevity and reduced SMR in comparison to the U.S. average population, and are more 

similar to a population of female NS, which is a strong indication that a healthy worker effect 

occurs for astronauts. The population effective dose (over 90 Sv for the astronaut cohort 

[Cucinotta, 2001; Cucinotta et al., 2008] ) is unlikely to have led to any increase in cancers at this 

time. We next discuss the NASA 2010 cancer risk assessment model (Cucinotta et al., 2011) and 

its application to PC estimates for International Space Station (ISS) and exploration missions. In 

the past, uncertainty analyses of PC estimates have been used to screen exposed persons for a 

potential causal relationship to an observed cancer, and in the determination of monetary 

compensation (NIH 2003; DHHS 2002). We describe point estimates and upper 95% confidence 

levels (CL) of the PC for 15 radiogenic tissue sites and a grouped ―remainder‖ category 

representing other cancer sites. Predictions for missions to the ISS, near Earth asteroids (NEAs),

and Mars are described, including comparisons for different ages of exposure and disease 

diagnosis. 

Astronauts censoring Flight Tragedies

Age, y

30 50 70 90 110

Survival

0.0

0.2

0.4

0.6

0.8

1.0

US Males

NS Males

Astronauts with 95% CI

Astronauts with Flight Tragedies

Age, y

30 50 70 90 110

Survival

0.0

0.2

0.4

0.6

0.8

1.0

US Males

NS Males

Astronauts with 95% CI

Figure 1. Kaplan-Meier survival versus age for astronauts and payload specialists compared to U.S. males and 

our projections for NS males. The left panel includes occupational deaths related to flight accidents or training, 

and right panel censors occupational deaths. Data from the Astronaut Fact Book (NASA 2005) and 

http://www.jsc.nasa.gov/Bios/.
3

Table 1a. Statistics for Fatalities Among Astronauts and Payload Specialists*

Category Total Frequency Death Frequency

Male Astronauts 269 41

Female Astronauts 40 3

Total Astronauts 316 43

Payload Specialist 23 1

Total 339 44

*Causes of death were 10 cancers, 4 circulatory diseases, 1 central nervous system, 19 occupational accidental 

deaths, 4 non-occupational accidental deaths, and 5 other causes of death. Data from the Astronaut Fact Book 

(NASA 2005) and http://www.jsc.nasa.gov/Bios/.

Table 1b. Standard Mortality Ratio (SMR) for Astronauts and Payload Specialists Relative to U.S. Average or NS

average (gender weighted to proportion of Male and Female astronauts) or Female NS, Suggests Astronauts have

Lifetime Risks Similar to Female NS

Comparison SMR P-value

Astronauts vs. U.S. avg. 0.67 [0.50, 0.90] 0.0006

Censoring tragedies vs. U.S. avg. 0.40 [0.27, 0.58] <10-6

Astronauts vs. NS avg. 0.88 [0.65, 1.18] 0.391

Censoring tragedies vs. NS avg. 0.52 [0.35, 0.76] 0.00067

Astronauts vs. Female NS 1.33 [0.99, 1.78] 0.0592

Censoring tragedies vs. Female NS 0.78 [0.53, 1.15] 0.215
4

2. Cancer Risk Projection Model

The instantaneous cancer incidence or mortality rates, I and M, respectively, are modeled as 

functions of dose D, or dose-rate Dr, gender, age at exposure aE, and attained age a or latency L, 

which is the time after exposure L=a-aE. The I (or M ) is a sum over rates for each tissue that 

contributes to risk, IT . These dependencies vary for each cancer type that could be increased by 

radiation exposure. The total risk of exposure-induced cancer (REIC) is calculated by folding the 

instantaneous radiation cancer incidence rate with the probability of surviving to time t, which is

given by the survival function S0(t) for the background population times the probability for radiation 

cancer death at previous time, and then integrating over the remainder of a lifetime:



 



E

t

E

a

M E

a

d z z a D

E I E REIC a D dt a a D S t e

( , , )

0

(1) ( , ) ( , , ) ( )





where z is the dummy integration variable. After adjustment for low dose and dose-rates though 

the dose and dose-rate effectiveness factor (DDREF) and radiation quality, the tissue-specific,

cancer incidence rate for an organ dose equivalent, HT , can be written as a weighted average of 

the multiplicative and additive transfer models, often called a mixture model:

DDREF

H

a a H v ERR a a a v EAR a a

T

IT E T T T E IT T T E

(2) ( , , ) [ ( , ) ( ) (1 ) ( , )]   0  

where vT is the tissue-specific transfer model weight, 0IT is the tissue-specific cancer incidence 

rate in the reference population, and where ERRT and EART are the tissue specific excess 

relative risk and excess additive risk per Sievert, respectively. The Hazard rates for cancer 

mortality M are modeled with similar approaches following the BEIR VII report (2006). Tissue 

weights assumed in the NASA 2010 model are shown in Table 2 along with recommendations 

from other reports. In the NASA 2010 Model (Cucinotta and Chappell, 2011; Cucinotta et al., 

2011), we used the United Nations Special Committee on the Effects of Atomic Radiation 

(UNSCEAR) report fitted excess additive risk (EAR) and excess relative risk (ERR) models for 

most tissue sites with the results from Preston et al. (2007) for a few tissues not reported by 

UNSCEAR. UNSCEAR employed Poisson maximum-likelihood methods and Bayesian analysis 

to represent dosimetry errors to fit generalized ERR and EAR models to the Life-Span Study of 

the Japanese atomic-bomb survivors (LSS) for cancer incidence for REIC. The ERR function 

fitted to the LSS data was:

(3) ( , , , ) ( ) exp[ 1 ln( ) ln( ) ln( )] 1 2 3 4

2

S E E

D ERR a aE L D D D e   a a  a  a

     

with a similar form for the EAR function. A linear dose response model provided the best fits to 

the tissue-specific cancer incidence data for solid cancers. For leukemias, the linear-quadratic 

model provided the best fit. The addition of the latency dependence, L=a-aE, was significant for 

several tissues, including EAR models for colon, breast, and non-melanoma skin cancer, and 

ERR and EAR functions for the category of all other solid cancer incidence. For breast and 

thyroid cancers, the NASA 2010 models follows BEIR VII, which recommended the use of 

results from a meta-analysis of several exposed cohorts, replacing results from the LSS with 
5

additive transfer models used for breast cancer (Preston et al., 2002) and multiplicative transfer 

models used for thyroid cancer (Ron et al., 1995). For estimating cancer risks for low dose or 

dose-rate exposures, NCRP Report 132 (NCRP 2000) used a DDREF of 2. The BEIR VII 

Report, recommended a DDREF of 1.5 based on Bayesian analysis of the LSS data and a 

select group of mouse tumor studies. The NIH uses (NIH 2003) a values close to 1.75, which is 

the choice for the NASA 2010 model (Cucinotta and Chappell, 2011).

Table 2. Tissue-Specific Transfer Weight T for Multiplicative Risk Transfer. Additive Risk Transfer Weight is then 

Given by 1-T. Values Described on page 126 of NCRP Report No. 132 (2000), and from pages 275-276 of BEIR 

VII (2006). 

Tissue NCRP No. 132 BEIR VII NASA 2010

Lung 0.5 0.3 0.5

Breast 0.5 0** 0**

Thyroid 0.5 1.0** 1.0**

Stomach, 

Colon, Liver, 

Esophagus

0.5 0.7 0.7

Leukemia 0.0 0.7 0.5

All Others 0.5 0.7 0.5

**Based on meta-analysis results described in BEIR VII.

Adjusting U.S. Cancer Rates for Never-Smokers Cancer Estimates

We estimated gender-specific NS cancer rates to represent a reference population by using 

age-specific rates for lung cancer and relative risk factors derived from literature searches for 

other cancers. Age- and gender-specific NS lung cancer rates were recently compiled by Thun 

et al. (2008) from an analysis of 13 cohorts and 22 cancer registries. These rates are used for 

our analysis of radiation lung cancer risks for NS. For other cancers, we use Center for Disease 

Control (CDC) estimates of proportions of cancer deaths for smokers (S) and former smokers 

(FS) in the U.S. population. CDC estimates (2010) of relative risks between these populations 

were used for cancers of the esophagus, stomach, bladder, and oral cavity, and for acute 

myeloid leukemia. We also considered other published sources for several tissue sites, which 

are liver, colorectal, and lymphomas (Liang et al., 2009; Sandler et al., 2003; IARC 1986). We 

estimated the fraction of cancers categorized in the ―remainder‖ category based on the number 

of cases reported by Preston et al. (2007) for different cancer types related to smoking including 

pharynx, larynx, and pancreas. Cancer rates reported for the U.S. population are made up of 

populations of S, FS, and NS, with proportions fS, fFS, and fNS, which leads to:

(4) ( ) ( ) ( ) ( ) 0 a f

0 a f0 a f0 a

NS

NS T

FS

FS T

S

 T S T    
6

The relative risks (RR) of S and FS compared to NS, RRS, and RRFS, respectively, are then 

used to compare rates for NS to the U.S. average rates,

( )

( )

(5) ( )

0

0

S S FS FS NS

NS T

T

RR f RR f f

a

a

 







We used the 2005 U.S. population data from surveillance, epidemiology, and end results (SEER)

(2006) and the CDC (2008) to represent the average U.S. population, and CDC estimates of 

fractions of populations for S, FS, and NS for males and females above age 40 y. The resulting 

estimates of RR for NS compared to the U.S. population are shown in Table 3. For NS risk 

estimates, we considered their longer lifespan due to their reduced mortality for cancer, and 

circulatory and pulmonary diseases. Age-specific rates for all causes of death for NS were not 

available; instead, we considered the survival probability for the average U.S. population and made 

adjustments for the age- and gender-specific rates for these diseases (CDC 2008; Malarcher et al., 

2000). Here we modified the survival probability in Eq. (1) to adjust for lower rates for cancers, and 

circulatory and pulmonary diseases that are also linked to tobacco use (CDC 2008).

Probability of Causation

The PC or AR is the fraction of the incidence of a disease in a population (exposed and nonexposed) that is due to radiation exposure. Thus, the PC represents the incidence of a disease 

in the population that would be eliminated if there were no radiation exposure. The PC is 

estimated from Eq. (1) by limiting the upper limit of integration to the date of disease diagnosis, 

aDiag for both the exposed population and the reference population, with the PC defined in terms 

of the conditional tissue specific ERR for each tissue:

1 ( , )

( , )

(6)

Diag

Diag

ERR T a

ERR T a

PC





where

1

( , ,0) ( )

( , , ) ( )

(7) ( , )

0

( , , )

0

 





 

Diag

E

Diag

E

t

E

a

M E

a

a

IT E

a

a

d z z a D

IT E

Diag

dt a a S t

dt a a D S t e

ERR T a






7

Table 3. Estimates of Relative Risk (RR) for Never-Smokers (NS) compared to Average U.S. Population for 

Several Cancers Related to both Smoking and Radiation Exposure

Relative Risk to Never-Smokers

Males Current smokers Former smokers Never- smokers RR(NS/US)

Esophagus 6.76 4.46 1 0.27

Stomach 1.96 1.47 1 0.71

Bladder 3.27 2.09 1 0.50

Oral Cavity 10.89 3.4 1 0.23

Liver 2.25 1.75 1 0.63

Colorectal 1.19 1.21 1 0.89

Leukemia 2 1.5 1 0.69

Remainder 4 2.5 1 0.43

Lung* 23.26 8.7 1 0.11

Females Current smokers Former smokers Never- smokers RR(NS/US)

Esophagus 7.75 2.79 1 0.35

Stomach 1.36 1.32 1 0.85

Bladder 2.22 1.89 1 0.65

Oral Cavity 5.08 2.29 1 0.46

Liver 2.25 1.75 1 0.67

Colorectal 1.28 1.23 1 0.88

Leukemia 2 1.5 1 0.74

Remainder 4 2.5 1 0.48

Lung* 12.69 4.53 1 0.23

*Lung data shown only for comparison, where risk calculations made using age-specific rates described in the text.

For males, current smokers, former smokers, and never-smokers are estimated at 24, 40, and 36% of the population 

above age 50 y. For females, we use 18, 35, and 47% for these percentages (CDC-MMWR, 2010).

3. Space Radiation and Organ Exposures

For calculations of space radiation tissue-specific cancer risks, Eq. (2) is used for the cancer 

incidence risk rate with the organ dose equivalent estimated using the high charge and energy 

transport computer code (HZETRN) (Wilson et al., 1994) with quantum multiple scattering 

fragmentation model (QMSFRG) cross-sections and Badhwar-O’Neill GCR environment

(Cucinotta, et al., 2011). For GCR, the use of risk assessment quantities based on absorbed 

dose is expected to have shortcomings and instead the NASA 2010 derived radiation quality 

descriptors of biological effectiveness based on particle track structure and fluence that were 

then expressed as radiation quality factors (Cucinotta et al., 2011). Here, a cancer risk crosssection representing the probability per particle is written as:

(8) ( , ) [ ( , ) (1 ( , )) ]

0

Z E 0 P Z E  P Z E L



   



with
8

m

Z P Z E e 









  



2

/

*2

(9) ( , ) 1



where the three parameters of the model (

/ 0

, m, and ) based on subjective estimates of 

results from radiobiology experiments. A radiation quality factor function is then found as:

LET

P E Z

QNASA P E Z

6.24( / ) ( , )

(10) (1 ( , ))

0 

  

The NASA quality factor depends on both particle charge number, Z and kinetic energy, E and not 

linear energy transfer, linear energy transfer (LET) alone as assumed in the International 

Commission on Radiological Protection (ICRP) definition of quality factors (ICRP 1990; ICRP 

2003; NCRP 2000). Distinct quality factors for estimating solid cancer and leukemia risk are used, 

Qsolid and Qleukemia, respectively. The parameters that enter Eq.’s (8) to (10) have straightforward

biophysical interpretations: 0 is the maximum value of the cross-section, which is related to 

maximum relative biological effectiveness (RBEmax) for the most biologically effective particle 

types; m is the slope of the cross-section for increasing ionization density;  determines the 

saturation value of the cross-section, where the relative biological effectiveness (RBE) begins to 

decline due to ―overkill‖ effects. is related to the initial slope of the gamma-ray dose response. 

Only the ratio 

/ 0

enters into model calculations, and not the individual values of these 

parameter. For solid cancer risks, radiobiology data is sparse. However, the largest RBE for HZE 

nuclei is in the range from 20 to 50 for solid tumors in rodents, and for chromosomal aberrations 

and mutations in human cells. A lower value is observed for leukemia (Weil et al., 2009). This 

assumes a linear dose response at low particle dose, ignoring non-targeted effects (NTEs) or 

other possible mechanisms that would lead to deviation from linearity at low fluence. Calculations 

with the NASA 2010 model include uncertainty analysis through the use of probability distribution 

functions (PDFs) to represent subjective assessments of ranges for each of the parameters with 

median values shown in Table 4. We also assume a description of ―thindown‖ at low energies, 

where the track width of a particle becomes smaller than the biological target. Here at low 

energies, the Risk cross-section is modified by the factor, PE=1-exp(-E/ETD) to account for 

thindown. The value of ETD=0.2 is based on experimental data for H and He. This factor has a 

very small impact for heavy ions since at low E they make a very small contribution to GCR or 

SPE exposures. The parameter  is assumed to have distinct values for light and heavy ions 

(Table 4).

Table 4. Cancer Risk Cross-Section or Quality Factor Parameters for Solid Cancer and Leukemia Risks*

Parameter Solid Cancer Leukemia

m 3 3

 550 (1000) 550 (1000)

0/, m

2 Gy 7000/6.24 1750/6.24

ETD 0.2 MeV/u 0.2 MeV/u

*Values in parenthesis for when distinct values for light ions (Z  4) are to be used. 
9

The cancer risk cross-section or related quality factor is expressed in terms of the track 

structure parameter, Xtr= Z*2/

2

, using the Barkas form for the effective charge function. The 

quality factor has an additional dependence on LET, which relates the particle track structure to 

the absorbed dose (Cucinotta et al., 2011). Figure 2 compares the NASA quality factor to the 

International Commission on Radiological Protection (ICRP) model used at NASA in the past for 

p, C, Si, and Fe nuclei versus LET illustrating the differences as described. The preferred slope 

on the rising side with increasing ionization density of m=3 is different than the ICRP Q(LET), 

which rises approximately as m=2. For calculations for a specific particle described by Z and E,

Eq. (2) is replaced by

(11) ZI (FT,aE,a)  I(aE,a)DT(E,Z)(1 P(Z,E))  (0/)P(Z,E)FT(Z,E)

where Iis the inner bracketed terms in Eq. (2) that contains the ERR and EAR functions for 

individual tissues. Using the HZETRN code or similar radiation transport codes, the fluence 

spectra, F(Xtr) can be found by transforming the energy spectra, j(E) for each particle, j of mass 

number and charge number, Aj and Zj respectively as:

(12) ( ) ( )

1

E

E

X

F X j

j

tr

tr 



 

















where we evaluate the Jacobian in Eq. (12) using the Barkas (1963) form for the effective 

charge number given by

(13) (1 )

2 / 3

* 125 / Z

Z Z e

 

 

The tissue-specific cancer incidence rate for GCR or SPEs can then be written:













      

j

IT I dE jT E S j E P Xtr dXtrF Xtr P Xtr (14) ( ) ( )(1 ( )) ( / ) ( ) ( )     0 

The first term on the right-hand side of Eq. (14) can be well approximated by the tissue 

averaged absorbed dose times the low LET risk coefficient. This approximation can be shown to 

lead to <10% over-estimation of its true value. However, in REIC calculations the error is even 

smaller because the second term of the right-hand side of Eq.(14) is dominant. We modified the 

HZETRN and BRYNTRN codes to perform the exact calculation; however, for the Monte-Carlo 

uncertainty analysis described below, we use the following form for the radiation cancer rate for 

the mixed particle and energy fields in space:

(14') IT I Dose 0  dXtrFLI (Xtr )PLI (Xtr ) dXtrFHI (Xtr )PHI (Xtr )       

where we distinguish spectra for light ions (Z≤4), FLI from heavy ions, FHI (Z>4). A summation over 

all cancer types is made for the radiation contribution to the survivor function in evaluating tissue 

specific risks, and a further summation over all cancer types to evaluate the overall cancer risk. 
10

In organ exposure evaluations, fluence spectra are averaged over each tissue using body 

shielding models. In Figure 3 we show differential REIC spectra versus Xtr at solar minimum 

behind increasing amounts of aluminum shielding for a Mars and an ISS mission. Calculations 

are made with the HZETRN code using the Badhwar and O’Neill GCR model (1992) and 

QMSFRG nuclear cross-section data base (Cucinotta et al., 2007). Results are shown on a 

linear-log plot such that the area under the curve for each decade of Xtr is equally weighted. 

Leukemia risk shows a reduced maximum Q-value compared to solid cancer risks, resulting in 

particles at lower values of Xtr making larger contributions compared to solid cancer risks. 

Figure 3 shows sharp spikes at increasing values of Z2for each GCR charge group. For 

example, at small values of Xtr we see peaks at 1 and 4, corresponding to protons and He 

nuclei. At large values of Xtr we observe a prominent peak near Z*2/

2

= 676 corresponding to 

relativistic Fe nuclei. These sharp peaks correspond to the contributions from relativistic 

particles, with broader peaks for deep space exposure due to contribution of low- to mediumenergy GCR not present in the ISS orbit due to the Earth’s geomagnetic field. 

Iron

LET, keV/m

100 1000 10000

Q

0

10

20

30

40

ICRP NASA Solid Cancer NASA Leukemia

Silicon

LET, keV/m

100 1000

Q

0

10

20

30

40

ICRP NASA Solid Cancer NASA Leukemia

Carbon

LET, keV/m

10 100 1000

Q

0

10

20

30

40

50

ICRP NASA Solid Cancer NASA Leukemia

Protons

LET, keV/m

1 10 100

Q

0

10

20

30

40

50

ICRP NASA Solid Cancer NASA Leukemia

Figure 2. Comparison of LET dependence for H, C, Si, and Fe nuclei in the proposed NASA Quality 

factors for solid cancer and leukemia risk estimation to quality factors from ICRP (ICRP, 1990).
11 

Z*2/2

1 10 100 1000 10000

(Z*2/%REIC

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Solid Cancer

Leukemia

Figure 3a. Leukemia and solid cancer risk distribution for 40-y Females versus Z*2/2

 on 6-month ISS 

mission at solar minimum with 20 g/cm2

 of aluminum shielding. 

Z*2/2

1 10 100 1000 10000

(Z*2/%REIC

0.0

0.8

1.6

2.4

3.2

4.0

Solid Cancer

Leukemia

Figure 3b. Leukemia and solid cancer risk distribution for 40-y Females versus Z*2/2

 for 30-month 

Mars mission including 18 month surface stay at solar minimum with 20 g/cm2

 of aluminum shielding.
12

In Figure 4a (Cucinotta et al., 2011), we compare calculations of annual Effective dose in the 

ICRP model to the NASA recommended model for ISS missions at solar minimum and maximum. 

Comparisons for aluminum and polyethylene shielding are shown. Figure 4b shows similar 

comparisons for 1-year in deep space. The ICRP model provides higher estimates at shallow 

shielding depth due largely to its higher estimation of contributions for relativistic particles than the 

NASA model. At deep shielding depths, the NASA model gives higher estimates due to its 

assignment of higher biological effectiveness to low energy proton and helium nuclei produced by 

neutrons and other particles and from atomic slowing-down of primaries. For the various mission 

and shielding scenarios, differences in Effective doses are on the order of 10 to 30%; however, 

the NASA model allows for an improved uncertainty assessment to be made than the ICRP Q 

function whose parameters are difficult to relate to biophysical interpretation. Of note is that 

shielding only provides a minor reduction in GCR organ dose equivalent. Most of the reduction 

occurs in the first 20 g/cm2of material at solar minimum. The reduced number of low energy 

particles at solar maximum reduces even this benefit from shielding. To significantly reduce GCR 

beyond this initial reduction would require several meters of hydrocarbon shielding.

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120 E, mSv

X, g/cm2

E(NASAQ)_Al, mSv

E(ICRP2007)_Al, mSv

E(NASAQ)_PE, mSv

E(ICRP2007)_PE, mSv

E(NASAQ)_Al, mSv

E(ICRP2007)_Al, mSv

E(NASAQ)_PE, mSv

E(ICRP2007)_PE, mSv

E(NASAQ)

E(ICRP2007 Wt)

1-yr at LEO at Solar Min 

1-yr at LEO at Solar Max 

Figure 4a. Comparison of annual Effective dose for males in ISS orbit (51.6 deg x 400 km) versus 

depth of shielding. Values for solar minimum and maximum are shown comparing ICRP model to 

recommended NASA model.
13

4. Uncertainty Analysis

To propagate uncertainties across multiple contributors, we performed Monte-Carlo simulations 

sampling over subjective PDFs that represent current knowledge of factors that enter into risk 

models (NCRP, 1997; 2006; Cucinotta et al., 2001, 2006; 2011). In a simplified manner, we can 

write a risk equation as a product of several factors including the dose, D, quality factor, Q, a 

low LET risk coefficient normally derived from the data of the atomic-bomb survivors, R0, and 

the dose and dose-rate reduction effectiveness factor, DDREF, that corrects risk data for doserate modifiers. Monte-Carlo uncertainty analysis uses the risk equation, but modified by normal 

deviates that represent subjective weights and ranges of values for various factors that enter 

into a risk calculation. First, we define XR(x) as a random variate that takes on quantiles x1, x2, 

..., xn such that p(xi) =P(X=xi) with the normalization condition  p(xi)=1. C(xi) is defined as the 

cumulative distribution function, C(x), which maps X into the uniform distribution U(0,1) and we 

define the inverse cumulative distribution function C(x)-1in order to perform the inverse mapping 

of U(0,1) into x: x=C(x)-1. Then we write for a simplified form of the risk equation for a MonteCarlo trial, :























DR

R phys Q

x

x x x

DDREF

FLQ Risk R age gender 0

(15) ( , )

0

where R0 is the low LET risk coefficient per unit dose, the absorbed dose, D is written as the 

product of the particle fluence, F and LET, L, and Q the radiation quality factor. The xR, xphys, xDr, 

and xQ are quantiles that represent the uncertainties in the low LET risk coefficient, the space 

physics models of organ exposures, dose-rate effects, and radiation quality effects, respectively. 

0

100

200

300

400

500

600

700

800

0 2 0 4 0 6 0 8 0 100 120 E, mSv

x, g/cm2

Effective dose for Male behind Shielding

Annual GCR at Solar Minimum

Aluminum

Polyethylene

E(NASA Q) E(ICRP2007 Q/Wt)

Annual GCR at Solar Maximum

Aluminum

Polyethylene

E(NASA Q) E(ICRP2007 Q/Wt)

Figure 4b. Annual GCR Effective doses in deep space versus depth of shielding for males. Values 

for solar minimum and maximum are shown comparing ICRP model to recommended NASA model.
14

Monte-Carlo trials are repeated many times, and resulting values binned to form an overall PDF 

taken into account each of the model uncertainties. In practice, the risk model does not use the 

simple form of Eq.(15). Instead, risk calculations are made using the REIC described by Eq. 

(1). For the 95% CIs for the %PC, we use the bootstrap method to infer the values from the 

uncertainty analysis for REIC.

PDF functions describing the uncertainties to the quantiles, x for the various parameters in the 

model are described in Table 5 from the recent report by Cucinotta et al. (2011). Two 

modifications are to introduce tissue-specific statistical uncertainties and to include uncertainties 

in the estimate of RR for NS compared to the U.S. average. The subjective PDFs are then 

employed in the Monte-Carlo calculation to describe a given space radiation scenario as 

described previously (Cucinotta et al., 2001; 2006; 2011). The point estimate for Qmax of 40, 

occurs for the most effective proton energy (~ 0.5 MeV). Values assigned give more weight to 

the animal model solid tumor data and are influenced by fractionation studies that suggest that 

higher RBEs are possible. The resulting PDF has a 95% CI for the maximum value of Q for solid 

cancer as [14, 70], which covers most of the range of values from Fe nuclei tumor induction and 

earlier neutron studies reflective of low energy protons. In Table 4, we use a GM=0.9 for the 

PDF associated with 0 / with the expectation that some tissues would have lower values as 

found for leukemia; however, there is a lack of information to make a more informed choice. In 

an alternative model of the radiation quality uncertainties, we assume that the slope, m is 

correlated with the position of the maximum value of Q as determined by the value of . After 

studying the functional dependence of the parameters of Eq. (10), we find the position of the 

maximum Q is held fixed for differential values of m if we use the constraint:

( 1)

4

(16) ( )

0





m

m





where 0 is the estimated value from Table 4. This alternative uncertainty assessment assumes 

that the kinetic energy for each Z at the maximum of the risk cross-section for cancer induction 

in humans is fairly well described by the existing data. In this approach, uncertainties in the 

maximum Q value, slope of Q with changing Xtr, and kinetic energy at the Q maximum are 

described; however, these values are more constrained compared to the uncertainty analysis 

without this constraint. The alternative uncertainty model was applied using conditional MonteCarlo sampling, where a random value of m is selected from its PDF, prior to sampling for the 

value with central estimate defined by Eq.(16).
15

Table 5. Summary of PDF for Uncertainty Components in NASA Model

Uncertainty Contribution PDF form for Quantile, xj Comment

Low LET Model:

Statistical Errors See Table 6 Tissue-specific values used

Statistical Errors in RR for NS Normal (M=1.0; SD=0.25) Applied to tissues considered in Table 3 

for NS

Bias in Incidence Data Normal (M=1.0; SD = 0.05) Based on NCRP Report 126

Dosimetry Errors Log-Normal (GM=0.9, GSD=1.3) Based on Preston et al. (2007); 

UNSCEAR (2008)

Transfer Model Weights Uniform distribution about preferred 

weight

Ignored for breast and thyroid cancers

DDREF Log-Normal (GM=1.0; GSD=1.4) DDREF=1.75; Truncated at 0.75 for 

inverse dose-rate probability <0.05

Risk Cross Section or Q:

0/ Log-normal(GM=0.9; GSD=1.4) GM<1 assumes existing data is biased 

to higher values

 Normal(M=1, SD=0.2) Position of peak estimates suggests 

variation on sensitivity, target size/ 

distributed targets

m Discrete m=[1.5,2,2.5,3.,3.5,4] with 

weights [.05,.1,.2,.4,.2.,.05]

Values restricted over (1.5,4)

Physics Uncertainties:

F(Z*2/

2

) for Z<5 Normal (M=1.05; SD=1/3) HZETRN does not account for mesons, 

e- and -rays that are low Charge and 

high velocity; may underestimate 

neutron recoils of low charge

F(Z*2/

2

) for Z5 Normal (M=1.0; SD=1/4) HZETRN accurate at high Z

Statistical Uncertainties for Tissue-Specific Estimates

For estimating the statistical uncertainties for overall cancer risks from radiation, we previously 

used (Cucinotta et al., 2011) the recommendations from NCRP Report 126 (1997) for the 

statistical uncertainty in the total cancer risk derived from cancer mortality data of the LSS 

survivors. However, larger statistical uncertainties occur for tissue-specific risk estimates 

derived from cancer incidence data. The various reports on tissue-specific estimates of cancer 

risks (BEIR VII, 2006; UNSCEAR 2008; Preston et al., 2007) typically combine statistical 

uncertainties with dosimetry or other uncertainties in reporting confidence levels. The 

UNSCEAR report did not report uncertainty ranges for their model EAR and ERR functions, 

which further complicates assessments of tissue-specific statistical uncertainties. The approach 

used here is to introduce subjective PDFs for the tissue-specific statistical uncertainties based 

on the Empirical Bayes (EB) results from Pawel et al. (2009). The important feature of the EB 

approach is to consider the correlation between the standards errors for different tissue sites

Table 6 shows results from this work. In the last two columns, we show results as the %SD

relative to the mean estimate, and the subjective values used in our analysis are shown in 

parenthesis. Note that statistical errors for leukemia were not considered by Pawel et al. (2009) 
16

and for thyroid and breast cancers, meta-analysis results that included data in addition to the 

Japanese atomic-bomb survivor data was considered as recommended by BEIR VII (2006). 

Table 6. Comparison of Maximum Likelihood Estimates (MLE) to Empirical Bayes (EB) Method for Gender 

Adjusted Site-specific ERR from the LSS Study (Pawel et al. 2008), and %SD Estimates and Subjective %SD 

Estimates Used for Model Calculations in Parentheses 

Tissue ERR/Sv Estimate

MLE EB

Standard Error

MLE EB

%SD EB

(subjective)

Stomach 0.32 0.32 0.06 0.06 0.19 (0.2)

Colon 0.49 0.47 0.11 0.10 0.21 (0.2)

Liver 0.31 0.32 0.10 0.09 0.28 (0.3)

Lung 0.70 0.63 0.13 0.11 0.18 (0.2)

Breast 0.67 0.63 0.10 0.09 0.14 (0.25)

Prostate 0.18 0.32 0.30 0.19 0.60 (0.6)

Uterus 0.04 0.05 0.05 0.05 1.0 (1.0)

Ovary 0.27 0.32 0.19 0.15 0.47 (0.5)

Bladder 0.84 0.58 0.29 0.18 0.31 (0.3)

Esophagus 0.63 0.48 0.31 0.19 0.40 (0.4)

CNS 0.37 0.38 0.17 0.14 0.37 (0.4)

Thyroid (0.4)

Oral Cavity 0.34 0.36 0.15 0.13 0.36 (0.4)

Remainder* 1.15 0.85 0.19 0.15 0.18 (0.2)

Leukemia (0.25)

*Remainder included different tissues in various reports described in text. 
17

5. Results 

Figure 5 illustrates some general characteristics of the %PC for different tissue sites. 

Calculations are shown for an organ dose equivalent of 0.5 Sv to each tissue site for a Female 

NS. Results for leukemia are made at the same the organ dose equivalent as solid cancers; 

however, the NASA model would assign a smaller Q-factor for leukemia resulting in a smaller 

GCR organ dose equivalent for the bone marrow compared to values for solid tissues. The left 

panel of Figure 5 shows calculation for an age of exposure of 40 y and the dependency of the 

%PC with time after exposure. Different trends occur for the tissues shown, which are largely 

determined by the age and time after exposure dependences of the fitted EAR and ERR 

functions, and the age dependence of the background rates for the tissues. Similar trends are 

observed for males (data not shown), The %PC peaks early after exposure for leukemia and 

stomach cancers and rises slowly with time after exposure for breast and lung cancers. There 

are additional uncertainties that are not accounted for with regards to the calculation of %PC 

less than 2 and 5 years after exposure for leukemia and solid cancer, respectively, because 

epidemiology data are often sparse at these times. In addition, heavy ion and neutrons appear 

to cause an earlier induction of cancers than low LET radiation, which is not accounted for in the 

current models of cancer risk (NCRP 2000; NCRP 2006). In the right panel of Figure 5, we 

show calculations of %PC at 20 years post-exposure for different ages of exposure. The %PC 

declines with age at exposure for most but not all tissues because background cancer risks tend 

to increase with age.

In Tables 7, 8, and 9, we show estimates of tissue-specific %REIC and 95% CL, and point 

estimates and upper 95% CL for %PC for a 1-year NEA mission, a 30-month Mars mission, and 

a 6-month ISS mission, respectively. Calculations for males and females represented by the 

40 y Female NS for Organ Dose Eq = 0.5 Sv

Years after Exposure, y

5 10 15 20 25 30 35 40 45

%PC

0

20

40

60

80

Stomach

Colorectal

Lung

Breast

Leukemia

Female NS at 20 y post Organ Dose Eq = 0.5 Sv

Age at Exposure, y

30 35 40 45 50 55 60

%PC

0

20

40

60

80

Stomach

Colorectal

Lung

Breast

Leukemia

Figure 5. Calculations of %PC for NS Females for organ dose equivalents of 0.5 Sv. The left panel 

shows results versus time after exposure and the right panel versus age at exposure for disease 

diagnosis at 20 y post-exposure.
18

U.S. average population or a population of NS are shown. For %PC estimates, results are 

shown for disease diagnosis at 20-year post exposure. Results are based on the UNSCEAR 

(2007) models for many tissues with the exceptions for noted before for breast, thyroid, and 

prostate cancer (Cucinotta et al., 2011). Similar results would be found using the BEIR VII 

models; however, the BEIR VII model did not include an age at exposure dependence for rates 

above age 30 y (BEIR VII), and therefore the NASA 2010 model used the UNCEAR models for

EAR and ERR rate functions. All results are for solar minimum conditions assuming 20 g/cm2

aluminum shielding. However, Figure 4 shows that larger amounts of shielding (up to 120 

g/cm2) would not change the results significantly, and a modest improvement is made by using 

polyethylene or water equivalent shielding compared to aluminum. Any time spent on 

extravehicular activities (EVAs) would marginally increase these results for solar minimum 

conditions. At solar maximum, the uncertainty in SPE environments is an important 

consideration (Kim et al., 2009). SPE’s uncertainties related to time of occurrence, total fluence,

and energy spectra play a much larger role for EVAs and will be considered elsewhere.

The estimates of PC for stomach and leukemia cancers show the largest association with GCR 

exposures followed by colon, liver, bladder, and lung cancers. An important gender dependence 

occurs between values for several tissues. For a Mars mission, estimates of the %PC at the 

95% confidence level would suggest a large proportion of cancers that would be observed in 

astronauts would be either caused or moved forward in time by radiation exposure. In contrast, 

a single ISS mission at solar minimum does not lead to %PC estimates above 50% for any 

tissue even at the 95% CL. However, for an individual making two or more ISS missions, PC 

values for leukemia and stomach cancers would likely exceed 50% at the 95% CL, and lung 

cancer PC estimates for NS would approach 50% at the 95% CL. The results shown in Table 8

are for the conjunction-type Mars mission. In comparison, the shorter opposition class missions 

have more time in deep space than the conjunction class missions. Thus, the conjunction class 

missions, which have long stays on the Mars surface, benefit from the 2 shielding provided by 

the solid body of Mars and its atmospheric shielding. The net effect is for a decreased risk for 

conjunction class missions compared to opposition class missions at solar minimum. The same 

is true at solar maximum because the risk of a high SPE exposure on the Mars surface is 

greatly reduced compared to the SPE risk in deep space. 
19

Table 7a. Lifetime %REIC and 95% CI, and %PC for 60 y Females Following 1-year NEA Mission at 

age 40 y. Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Females NS Females

Stomach 0.331 [0.091, 1.31] 54.0 (82.2) 0.347 [0.095, 1.39] 57.6 (84.5)

Colon 0.461 [0.135, 1.79] 21.1 (51.0) 0.439 [0.128, 1.71] 21.2 (51.1)

Liver 0.117 [0.026, 0.459] 27.7 (60.0) 0.117 [0.026, 0.466] 34.6 (67.9)

Lung 2.06 [0.602, 8.02] 21.6 (51.7) 0.815 [0.238, 3.20] 41.2 (73.3)

Breast 0.737 [0.215, 2.88] 5.98 (19.9) 0.779 [0.228, 3.00] 5.99 (20.1)

Uterus 0.133 [0.0, 0.791] 5.73 (26.6) 0.140 [0.0, 0.835] 5.74 (26.6)

Ovary 0.156 [0.0, 0.693] 11.7 (37.1) 0.165 [0.0, 0.742] 11.7 (37.3)

Bladder 0.331 [0.079, 1.29] 19.0 (47.6) 0.299 [0.071, 1.18] 22.1 (53.0)

Esophagus 0.034 [0.006, 0.141] 15.7 (43.5) 0.016 [0.003, 0.069] 23.8 (56.7)

Brain-CNS 0.044 [0.007, 0.183] 12.1 (36.5) 0.046 [0.007, 0.194] 12.1 (36.7)

Thyroid 0.123 [0.021, 0.516] 12.1 (36.6) 0.126 [0.021, 0.531] 12.1 (36.7)

Oral Cavity 0.022 [0.004, 0.090] 5.70 (20.1) 0.022 [0.004, 0.094] 11.6 (35.5)

Remainder 0.479 [0.132, 1.90] - 0.242 [0.066, 0.958] -

Leukemia 0.392 [0.142, 1.38] 57.3 (82.5) 0.344 [0.125, 1.22] 62.2 (85.5)

Total 5.71 [1.77, 20.8] - 4.11 [1.35, 15.1] -

Table 7b. Lifetime %REIC and 95% CI, and %PC for 60 y Males Following 1-year NEA Mission at age 

40 y. Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Males NS Males

Stomach 0.316 [0.087, 1.23] 38.7 (71.0) 0.329 [0.085, 1.29] 45.8 (76.7)

Colon 0.491 [0.135, 1.89] 20.8 (50.3) 0.485 [0.133, 1.87] 20.8 (50.3)

Liver 0.141 [0.031, 0.549] 12.3 (35.3) 0.131 [0.029, 0.513] 14.9 (40.8)

Lung 0.750 [0.206, 2.88] 6.87 (22.1) 0.386 [0.106, 1.50] 30.1 (62.6)

Prostate 0.289 [0.0, 1.38] 1.94 (8.63) 0.319 [0.0, 1.53] 1.94 (8.66)

Bladder 0.677 [0.150, 2.61] 14.6 (39.7) 0.466 [0.103, 1.82] 16.7 (43.9)

Esophagus 0.109 [0.016, 0.45] 11.4 (34.7) 0.037 [0.006, 0.151] 14.0 (40.1)

Brain-CNS 0.045 [0.007, 0.186] 9.40 (30.0) 0.049 [0.007, 0.201] 9.41 (30.1)

Thyroid 0.024 [0.004, 0.1] 6.26 (21.5) 0.026 [0.004, 0.107] 6.27 (21.5)

Oral Cavity 0.019 [0.004, 0.076] 1.83 (6.94) 0.020 [0.004, 0.082] 7.32 (24.6)

Remainder 0.419 [0.108, 1.65] - 0.198 [0.051, 0.771] -

Leukemia 0.612 [0.211, 2.13] 59.9 (83.4) 0.537 [0.186, 1.89] 66.1 (87.3)

Total 4.00 [1.31, 14.6] - 3.07 [1.01, 11.2] -
20

Table 8a. Lifetime %REIC and 95% CI, and %PC for 60 y Females Following 30-month Mars Mission at 

age 40 y. Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Females NS Females

Stomach 0.556 [0.153, 2.15] 66.8 (88.6) 0.585 [0.161, 2.31] 70.0 (90.2)

Colon 0.762 [0.223, 2.91] 31.3 (63.5) 0.728 [0.213, 2.82] 31.4 (63.9)

Liver 0.198 [0.044, 0.769] 39.7 (71.9) 0.198 [0.044, 0.792] 47.7 (78.5)

Lung 3.46 [1.010, 12.5] 31.9 (63.7) 1.37 [0.401, 5.22] 54.3 (81.9)

Breast 1.18 [0.345, 4.44] 9.65 (28.7) 1.25 [0.366, 4.83] 9.67 (29.2)

Uterus 0.223 [0.0, 1.32] 9.43 (38.1) 0.235 [0.0, 1.39] 9.45 (38.2)

Ovary 0.261 [0.0, 1.13] 18.5 (49.7) 0.278 [0.0, 1.22] 18.5 (49.9)

Bladder 0.559 [0.134, 2.07] 28.7 (59.8) 0.508 [0.122, 1.93] 32.8 (65.0)

Esophagus 0.057 [0.010, 0.234] 24.1 (56.4) 0.028 [0.005, 0.113] 34.8 (68.6)

Brain-CNS 0.072 [0.012, 0.294] 18.8 (48.6) 0.076 [0.013, 0.314] 18.9 (49.0)

Thyroid 0.187 [0.032, 0.788] 18.7 (49.1) 0.194 [0.033, 0.818] 18.7 (49.3)

Oral Cavity 0.035 [0.007, 0.143] 9.14 (29.0) 0.037 [0.007, 0.152] 17.9 (47.5)

Remainder 0.777 [0.213, 2.95] - 0.394 [0.108, 1.52] -

Leukemia 0.651 [0.236, 2.24] 69.5 (88.7) 0.573 [0.208, 2.0] 73.6 (90.7)

Total 9.41 [3.09, 33.1] - 6.78 [2.22, 24.3] -

Table 8b. Lifetime %REIC and 95% CI, and %PC for 60 y Males Following 30-month Mars Mission at age 

40 y. Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Males NS Males

Stomach 0.530 [0.145, 2.05] 52.1 (80.8) 0.555 [0.152, 2.18] 59.2 (85.1)

Colon 0.816 [0.238, 3.14] 30.9 (63.3) 0.807 [0.221, 3.11] 31.0 (63.3)

Liver 0.237 [0.053, 0.918] 19.4 (48.2) 0.221 [0.049, 0.87] 23.1 (54.3)

Lung 1.26 [0.369, 4.71] 11.2 (32.0) 0.652 [0.179, 2.47] 42.4 (73.6)

Prostate 0.483 [0.0, 2.27] 3.25 (13.6) 0.535 [0.0, 2.53] 3.25 (13.7)

Bladder 1.15 [0.254, 4.34] 22.7 (52.7) 0.792 [0.175, 3.01] 25.6 (56.7)

Esophagus 0.184 [0.031, 0.749] 18.1 (47.3) 0.062 [0.009, 0.252] 21.8 (53.1)

Brain-CNS 0.073 [0.012, 0.298] 14.9 (41.6) 0.080 [0.013, 0.326] 14.9 (41.8)

Thyroid 0.038 [0.006, 0.157] 10.2 (32.0) 0.041 [0.007, 0.170] 10.2 (32.0)

Oral Cavity 0.031 [0.006, 0.122] 3.03 (11.1) 0.032 [0.006, 0.132] 11.8 (35.3)

Remainder 0.683 [0.188, 2.60] - 0.325 [0.089, 1.23] -

Leukemia 1.02 [0.351, 3.50] 71.8 (89.8) 0.891 [0.308, 3.09] 76.9 (92.0)

Total 6.70 [2.20, 23.6] - 5.15 [1.69, 18.3] -
21

Table 9a. Lifetime %REIC and 95% CI, and %PC for 60 y Females Following ISS Mission at age 40 y. 

Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions with Mission Length of 180 d.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Females NS Females

Stomach 0.031 [0.008, 0.126] 9.75 (30.4) 0.032 [0.008, 0.131] 11.1 (33.7)

Colon 0.043 [0.012, 0.172] 2.38 (8.88) 0.041 [0.011, 0.161] 2.39 (8.82)

Liver 0.011 [0.002, 0.045] 3.41 (12.6) 0.011 [0.002, 0.044] 4.67 (16.6)

Lung 0.192 [0.053, 0.765] 2.45 (9.10) 0.075 [0.021, 0.304] 5.99 (20.4)

Breast 0.069 [0.019, 0.276] 0.57 (2.24) 0.072 [0.020, 0.291] 0.57 (2.26)

Uterus 0.012 [0.0, 0.075] 0.56 (3.26) 0.013 [0.0, 0.078] 0.56 (3.26)

Ovary 0.015 [0.0, 0.066] 1.20 (5.25) 0.015 [0.0, 0.070] 1.20 (5.27)

Bladder 0.031 [0.007, 0.126] 2.12 (8.02) 0.028 [0.006, 0.113] 2.56 (9.57)

Esophagus 0.003 [0.001, 0.014] 1.68 (6.79) 0.002 [0.000, 0.006] 2.78 (10.8)

Brain-CNS 0.004 [0.001, 0.017] 1.22 (5.01) 0.004 [0.001, 0.018] 1.22 (5.01)

Thyroid 0.011 [0.002, 0.048] 1.21 (4.95) 0.012 [0.002, 0.049] 1.21 (4.93)

Oral Cavity 0.002 [0.001, 0.009] 0.59 (2.21) 0.002 [0.001, 0.009] 1.21 (4.68)

Remainder 0.045 [0.012, 0.180] - 0.022 [0.006, 0.091] -

Leukemia 0.036 [0.012, 0.129] 10.9 (30.4) 0.031 [0.011, 0.113] 13.0 (35.0)

Total 0.529 [0.164, 1.99] - 0.379 [0.117, 1.43] -

Table 9b. Lifetime %REIC and 95% CI, and %PC for 60 y Males Following ISS Mission at age 40 y. 

Calculations Assume 20 g/cm2 Aluminum Shielding for Solar Minimum Conditions with Mission Length of 180 d.

Tissue %REIC %PC

(95% CL)

%REIC %PC

(95% CL)

U.S. Ave. Males NS Males

Stomach 0.029 [0.008, 0.117] 5.52 (18.9) 0.031 [0.008, 0.122] 7.25 (23.7)

Colon 0.045 [0.012, 0.179] 2.34 (8.64) 0.045 [0.012, 0.174] 2.35 (8.54)

Liver 0.013 [0.003, 0.053] 1.28 (4.91) 0.012 [0.003, 0.049] 1.59 (6.08)

Lung 0.070 [0.019, 0.274] 0.67 (2.59) 0.036 [0.010, 0.142] 3.80 (13.5)

Prostate 0.027 [0.0, 0.130] 0.18 (0.87) 0.029 [0.0, 0.143] 0.18 (0.87)

Bladder 0.064 [0.014, 0.252] 1.56 (5.91) 0.044 [0.010, 0.173] 1.82 (6.87)

Esophagus 0.010 [0.002, 0.043] 1.18 (4.77) 0.003 [0.001, 0.014] 1.47 (5.87)

Brain-CNS 0.004 [0.001, 0.017] 0.93 (3.79) 0.004 [0.001, 0.019] 0.93 (3.78)

Thyroid 0.002 [0.000, 0.010] 0.61 (2.48) 0.002 [0.000, 0.010] 0.61 (2.46)

Oral Cavity 0.003 [0.001, 0.008] 0.23 (0.71) 0.002 [0.001, 0.008] 0.77 (2.92)

Remainder 0.039 [0.010, 0.155] - 0.018 [0.005, 0.073] -

Leukemia 0.056 [0.019, 0.198] 12.0 (32.7) 0.049 [0.017, 0.175] 15.1 (38.9)

Total 0.372 [0.122, 1.38] - 0.284 [0.093, 1.05] -
22

6. Discussion and Conclusions

The results reported here suggest that a large portion of cancers that would be observed in 

crews after long-term missions to NEAs or Mars could be attributed to GCR exposure. However, 

it should be noted that current estimates of 95% confidence levels for the 3% REID limit would 

restrict deep space missions to 4 to 8 months depending on age, gender, and prior exposures.

For example, 40-y NS males and females with small prior exposures would be limited to 7 and 8

months, respectively, with heavy shielding at solar minimum. Thus, the %PC at the maximum 

allowed mission length would be reduced by about 30% from the values in Table 7 for an NEA 

mission near solar minimum. In contrast, PC estimates for ISS missions are not estimated to be 

significant due to the smaller mission length and because of the larger fraction of crew 

exposures from the higher energy GCR and trapped protons, which have smaller quality factors 

and uncertainties compared to the full GCR spectrum in deep space. The deep space GCR 

environment contains a larger fraction of particles with kinetics energies below 1000 MeV/u than 

the ISS orbit, and HZE nuclei at these energies are expected to have the maximum biological 

effectiveness (Cucinotta et al., 2011).

The majority of astronauts are surely classified as ―healthy workers‖ based on established 

evidence of optimal nutrition, exercise, medical care, and NS status, thereby reducing cancer 

risks. This leads to the paradoxical result that radiation cancer risks are estimated to be 

significantly reduced for NS and healthy workers such as astronauts compared to the average 

U.S. population, while probability of causation estimates for several cancer types are increased. 

Furthermore, the use of an NS population to represent astronauts may lead to an underestimation of PCs, which is suggested by Kaplan-Meir survival analysis and SMR results of 

Figure 1 and Table 1. These results suggest that adjustment for smoking effects does not 

account for the entire increase in longevity or reduced SMR found for astronauts at this time.

Because multiplicative risk transfer models are most often used for solid cancer risk estimates, 

further research on categorizing healthy worker effects could play a significant role in both 

radiation risk and probability of causation estimates. In fact, the level of risk reduction predicted 

for NS compared to the average U.S. population is greater than the organ dose equivalent 

reduction that would be provided to crew by more than 1 meter of polyethylene or water 

shielding. This suggests that research into healthy workers effects could lead to substantial cost 

reduction for a NEA or Mars missions, because of the very large cost to launch shielding into 

deep space in comparison to the costs of research efforts. Research into uncertainty reduction 

remains the principal approach to improvements in this and other areas of radiation risk 

estimation and mitigation. 

Probability of causation provides an indicator of possible association; however, clearly other 

information should be collected to ascertain potential causal relationships. A %PC above 50%

either at the point estimate or at the 95% or even 99% CL has been used in compensation of 

workers including military and nuclear reactor workers (DHHS 2002; NIH 2003; Leigh and 

Wakeford, 2001). Family history and an individual’s possible exposure to other carcinogens 

should be considered in an assessment of possible causality. The use of family history data 

should consider the possibility that genetic predisposition of specific cancer types (NCRP 2011) 

may also confer increased radiation sensitivity. Other factors to be considered include smoking 
23

history, which effects lung, esophagus, oral, bladder, and several other cancers, and reproductive 

history, which can impact the risk of breast and other cancers in women (NIH 2003).

Stomach cancer and leukemia have the largest PC values, and astronauts that participate in 

two or more ISS missions could reach a significant PC for these tumor types. Our estimates 

used the NASA 2010 model, which assigns a smaller quality factors for leukemia compared to 

solid cancers. An even higher PC for leukemia would be predicted if the ICRP-60 quality factors 

were assumed. For stomach cancer risk estimates, we used the BEIR VII (2006) 

recommendation for tissue weighting factors for the relative contributions for multiplicative and 

additive risk transfer for stomach cancer. It is known that the use of the additive risk transfer 

model based on the Japanese A-bomb survivor data leads to a much higher risk estimate for 

stomach cancers in the U.S. population (NCRP, 1997) than the multiplicative transfer model. 

Studies of solid tumor risks across different strains of mice are supportive of multiplicative risk 

transfer (Storer et al., 1988). The BEIR VII report (2006) recommended higher transfer weights 

for multiplicative risk transfer than earlier reports. This is a good example of the importance of 

improving the understanding of the extrapolation of radiation data between populations and from 

experimental results to humans because the choice of transfer models can widely change REIC

and PC estimates for exploration missions. 

The discovery of biomarkers of radiation-induced cancers is an outstanding problem. Studies of 

cytogenetic signatures of thyroid cancer were reported (Nikiforov et al., 1997), but very little 

information is available for other tissues. In recent years, molecular signatures of radiation 

causality have been investigated, including transcriptome analysis (Detours et al., 2005; Port et 

al., 2007; Ugolin et al., 2011), but have led to conflicting results. For space radiation exposures,

research into biomarkers of causality is more challenging because of the types of radiation in 

space and lack of any human data. Approaches based on experimentally produced tumors in 

animal models should be considered, and will require improved understanding of methods of 

extrapolation to humans. 

In future work, the approach used here will be extended to include other cancer types including 

non-melanoma skin cancer (Kim et al., 2006), bone cancer, and components of the remainder 

term, which includes renal, gallbladder, pancreatic, larynx, and several other cancers. Also, 

information of the components of leukemia and lung cancer risks should be considered and may 

allow for PC estimates based on distinct histological types for these tumors. In addition, more 

extensive Monte-Carlo evaluations should be made to report 99% CIs, which are used in 

screening terrestrial radiation workers for possible compensation considerations. Reduction in 

the uncertainties in projecting space radiation risks through further cost-effective research will

have the largest impact on these challenges to NASA and space exploration. Finally, it should 

be noted that NASA policy to limit REID to 3% at the 95% CL strongly overlaps with a goal of 

ensuring estimates of the PC for most cancer types do not reach a level of significance. 
24

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February 2012 NASA Technical Memorandum

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Probability of Causation for Space Radiation Carcinogenesis following International Space 

Station, Near Earth Asteroid, and Mars Missions 

6. AUTHOR(S)

Francis A. Cucinotta; Myung-Hee Y. Kim; Lori J. Chappell

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13. ABSTRACT (Maximum 200 words)

Cancer risk is an important concern for International Space Station (ISS) missions and future exploration missions. An important 

question concerns the likelihood of a causal association between a crew members’ radiation exposure and the occurrence of cancer. 

The probability of causation (PC), also denoted as attributable risk, is used to make such an estimate. This report summarizes the 

NASA model of space radiation cancer risks and uncertainties, including improvements to represent uncertainties in tissue-specific 

cancer incidence models for never-smokers and the U.S. average population. We report on tissue-specific cancer incidence estimates 

and PC for different post-mission times for ISS and exploration missions. An important conclusion from our analysis is that the NASA 

policy to limit the risk of exposure-induced death to 3% at the 95% confidence level largely ensures that estimates of the PC for most 

cancer types would not reach a level of significance. Reducing uncertainties through radiobiological research remains the most 

efficient method to extend mission length and establish effective mitigators for cancer risks. Efforts to establish biomarkers of space 

radiation-induced tumors and to estimate PC for rarer tumor types are briefly discussed.

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long duration space flight; radiation dosage; radiation effects; cancer; extraterrestrial 

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