Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Esophagus Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 1 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Colon Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 2 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Stomach Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 3 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

All digestive Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 4 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Pancreas Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 5 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Liver Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 6 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Gallbladder Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 7 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Bladder Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 8 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Urinary organs (excl. Bladder) Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 9 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

All Male Genitalia (incl. Prostate) Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 10 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Oral Cavity and Pharynx Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 11 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Nervous system Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 12 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Lymphoma & multiple myeloma Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 13 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Breast Cancer Model

20 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 14 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Breast Cancer Model

20 years latency

Inputs: Female, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 15 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Ovary Cancer Model

20 years latency

Inputs: Female, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 16 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Lung Cancer Model

20 years latency

Note: Because there is no adjustment for age at diagnosis for this scenario the two curves overlap.

Inputs: Male, Never smoked, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 17 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Lung Cancer Model

20 years latency

Inputs: Male, Former smoker, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 18 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Lung Cancer Model

20 years latency

Inputs: Male, Current smoker (? cig/day), One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 19 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Bone Cancer Model

10 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 20 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Thyroid Cancer Model

10 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 21 of 22
Note: The graph on this page depicts the probability of causation for acute exposures to high 

energy (i.e., >250 keV) photons. Because radiation exposure types (i.e., alpha, beta or gamma) 

and exposure scenarios (i.e., chronic vs. acute) vary widely among individuals, these graphs 

can not be used for estimating the outcome of a specific case. Rather, they are provided to 

allow one to compare the variability in dose response among the various cancer models 

employed in IREP.

Leukemia (excl. CLL) Cancer Model

10 years latency

Inputs: Male, One acute exposure, photons E>250keV, Constant distribution

Age at Diagnosis 40 years 50 years PC (99th percentile)

0

10

20

30

40

50

60

70

80

90

100

Organ Dose (rem)

0 10 20 30 40 50 60 70 80 90 100

Page 22 of 22