DEVELOPMENT OF A RISK MODEL FOR CHRONIC LYMPHOCYTIC 

LEUKEMIA FOR NIOSH-IREP 

January 5, 2010 

This document was prepared by J. R. Trabalka and A. I. Apostoaei of SENES Oak Ridge, Inc., 

under NIOSH contract 200-2006-18097. This final version has been slightly edited by NIOSH 

during the internal review process.
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TABLE OF CONTENTS 

1.0 PURPOSE AND APPROACH ............................................................................................3 

2.0 BACKGROUND .................................................................................................................4 

3.0 CHARACTERISTICS, CLINICAL FEATURES, AND ORIGINS OF CLL .....................6 

 3.1 What is CLL? ...........................................................................................................6 

 3.2 Origins and Etiology ................................................................................................6 

 3.3 Characteristics and Relationship to SLL ..................................................................8 

 3.4 Implications for Assigning a Target Organ for Dose Reconstruction .....................9 

4.0 CLASSIFICATION AND DIAGNOSIS OF CLL ............................................................11 

5.0 DEVELOPMENT OF A RISK MODEL FOR CLL .........................................................13 

5.1 Selection of Risk Coefficients ...............................................................................13 

5.2 Concerns Associated with Use of the Lymphoma and 

 Multiple Myeloma Model ......................................................................................15 

5.3 Handling of Risk Transfer Between Populations ...................................................19 

5.4 Latency ...................................................................................................................20 

5.4.1 Handling of Latency in the CLL Model ..................................................22 

5.5 Sample Results from the Proposed CLL Model ....................................................24 

6.0 DOSE RECONSTRUCTIONS FOR NUCLEAR ENERGY WORKERS 

 FOR USE WITH THE CLL MODEL ...............................................................................26 

6.1 Background ............................................................................................................26 

6.2 Challenges in Estimating Meaningful Doses for Use with 

 the IREP CLL Model .............................................................................................29 

7.0 CONCLUSIONS................................................................................................................34 

8.0 REFERENCES ..................................................................................................................35 

ACKNOWLEDGEMENTS ...........................................................................................................45 

APPENDIX A Age and Gender Dependence of CLL Incidence Rates in the U.S. and Japan ......46 

APPENDIX B Additional Background on Etiology of CLL .........................................................52 

APPENDIX C Assessment of Potential Latency for CLL, Lymphomas, and 

 Multiple Myeloma .................................................................................................55 

 
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1. PURPOSE AND APPROACH 

SENES Oak Ridge, Inc. was asked by the Office of Compensation Analysis and Support 

(OCAS) of the National Institute of Occupational Safety and Health (NIOSH) to investigate the 

possibility of developing a radiogenic risk model for chronic lymphocytic leukemia (CLL) for 

potential inclusion in the computer program NIOSH-IREP. Adaptation of the risk model for the 

lymphoma and multiple myeloma grouping in NIOSH-IREP was considered to be an option for a 

CLL risk model because CLL is now classified as a form of non-Hodgkin’s lymphoma (NHL). 

The lymphoma and multiple myeloma model in NIOSH-IREP is based primarily on Japanese 

atomic-bomb survivor data for non-Hodgkin’s Lymphoma (NHL), but also on data for 

Hodgkin’s disease (also a lymphoma) and multiple myeloma (ICD-9 codes 200−203) (Land et al. 

2003; UNSCEAR 2008). 

This model has been used by the Veteran’s Administration (VA) under the atomic veterans 

compensation program to assess the probability of causation (PC) of CLL occurring in military 

personnel who were exposed to radiation during tests or uses of atmospheric nuclear weapons. 

Currently, the PC of CLL occurring in these military personnel is estimated using the highest of 

the reconstructed doses to one of three organs: red bone marrow, spleen, or thymus. The 

rationale behind the VA’s approach is that since CLL is now considered to be a form of NHL, 

red bone marrow is not the sole target organ (see more below). 

We reviewed key papers on the epidemiological, molecular, and clinical basis of CLL, 

including, but not limited to those cited by Richardson et al. (2005), by John Boice (in his 

January 7, 2005, response to NIOSH/OCAS on reconsideration of CLL for purposes of 

compensation; Boice 2005), in the draft NIOSH Annotated Bibliography for CLL (NIOSH 

2004), Silver et al. (2007), the CLL Issue of the British Journal of Haematology (Volume 135, 

No. 5, 2007), and by the BEIR VII committee (NRC 2006). The latter included an additional 

paper (Hall et al. 1992) that examined dose-responses and latency for CLL in patients exposed to 

I-131 in diagnostic or therapeutic procedures. We also compiled information on sex and agespecific background incidence rates for CLL from the SEER registry for the U.S. population 

(SEER 2005) and from the IARC data bases for the Japanese populations (Parkin et al. 1997) 

(Appendix A). We also evaluated data potentially bearing on the issue of latency of radiogenic 

CLL from epidemiological studies. 
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The evaluated data was used to create a risk model for CLL by modifying the risk model for 

lymphoma and multiple myeloma obtained from an evaluation of the Japanese atomic-bomb 

survivor data from the Life-Span Study (LSS) cohort by Land et al. (2003) to include an 

extended latency period with greater uncertainty than that currently used in IREP. Alternatives to 

the risk transfer module used for lymphoma and multiple myeloma in IREP were considered but 

rejected because of limitations of extant epidemiological studies. This risk model is proposed to 

be used on an interim basis, at least until a model based on DS02 data for incidence of NHL in 

the LSS cohort becomes available. 

2. BACKGROUND 

The occurrence of lymphomas, and of CLL in particular, has not been convincingly linked 

to radiation exposure (see, e.g., IARC 2000; Preston et al. 1994; Boice 2002, 2005; NIOSH 

2004; NRC 2006; Cardis et al. 2007; Schubauer-Berigan et al. 2007; Silver et al. 2007; Hamblin 

2008; UNSCEAR 2008; Vrijheid et al. 2008; El Ghissassi et al. 2009). An inability to discern a 

statistically significant increased risk for CLL from radioepidemiological studies had led to the 

widespread conclusion that CLL is not radiogenic (Boice 2002, 2005). However, it has been 

argued that a number of factors (including diagnostic misclassification; see Sect. 4), coupled 

with limited follow-up periods in epidemiological studies and the possibility of an extended 

latency period for induction of CLL, have confounded previous attempts to determine whether 

CLL is radiogenic (Crowther 2004; Richardson 2004; Richardson et al. 2005; Schubauer-Berigan 

et al. 2007; Silver et al. 2007; Hamblin 2008). In addition, others have argued that, despite the 

current lack of evidence from epidemiological studies, mechanistic evidence from genetic and 

molecular studies indicates that CLL should, in principle, be inducible by radiation exposure 

(Crowther 2004; Richardson 2004; Richardson et al. 2005; Zablotska 2004; Rai 2007a). 

These arguments appear to be supported by a variety of new information, including the 

recent demonstration by Richardson et al. (2009) of a significant positive association between 

NHL and ionizing radiation in the Japanese atomic bomb survivors based on extended follow-up. 

Suggestive but as yet inconclusive findings in a number of recent epidemiological studies 

indicate that the evidence for CLL as a radiogenic disease is growing (see, e.g., Linet et al. 2005; 

Rericha et al. 2006; Schubauer-Berigan et al. 2007; Abramenko et al. 2008; Hamblin 2008; 
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Kesminiene et al. 2008; Romanenko et al. 2008). Although these studies do not currently provide 

the basis for a radiation risk model for CLL, and contrary findings (i.e., no significant association 

between radiation and CLL) have been reported from recent comprehensive studies of radiation 

workers (Vrijheid et al. 2008; Muirhead et al. 2009), we agree with Hamblin’s (2008) conclusion 

that “[i]rradiation may have been given a clean bill of health with respect to CLL with less than 

adequate evidence.” 

Data on cancer incidence in the LSS cohort from 1950 through 1987 (Preston et al. 1994) 

provided the basis for development of risk models for leukemia, lymphomas, and multiple 

myeloma used in IREP (Land et al. 2003). However, only 4 cases of CLL were reported in the 

exposed and unexposed groups combined; this number was too small to develop a meaningful 

risk model for CLL. 

The incidence of CLL in the Japanese population is very low and CLL is primarily a disease 

of old age in both the U.S. and Japan (Appendix A). Using the age- and sex-specific incidence 

rates for CLL in the Japanese population as a whole in Table A.1, combined with information on 

age at exposure, sex, vital status, and overall population size for the LSS cohort from Fig.1 in 

Preston et al. (1994), we estimated that about one case of CLL (0.76; 95% C.I. 0.60, 0.93) might 

have been expected in the LSS cohort over the 37-y period from 1950−1987, in the absence of 

radiation exposure. A similar estimate based on cancer incidence data for Hiroshima and 

Nagasaki in Table A.2 was slightly higher (1.2 cases; 95% C.I. 0.78, 1.76). Our estimate of the 

expected number of cases of CLL in unexposed persons is thus about one-fourth of the number 

observed in the LSS cohort as a whole. 

Although this comparison suggests that there could be a positive association between 

radiation and incidence of CLL in the LSS cohort, despite the small number of cases involved, 

the published information on the LSS cohort does not provide data on radiation doses or 

characteristics of the exposed individuals (gender, age at exposure, or attained age) who 

developed CLL. Thus, we are prevented from drawing conclusions about the radiogenicity of 

CLL from this comparison. From an epidemiological perspective, we can only conclude that we 

currently do not have scientific evidence from the LSS data to say that CLL is radiogenic, not 

that there is no risk of radiation-induced CLL in the LSS cohort or in occupationally exposed 

persons (also see Zablotska 2004; Rai 2007a; Hamblin 2008). 
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3. CHARACTERISTICS, CLINICAL FEATURES, AND ORIGINS OF CLL 

3.1 What is CLL? 

Chronic lymphocytic leukemia is a disorder of morphologically mature but less 

immunologically mature lymphocytes and is manifested by progressive accumulation of these 

cells in the blood, bone marrow, and lymphatic tissues (Chiorazzi et al. 2005; NCI 2009b). It 

appears etiologically and clinically to be a lymphoma and thus differs from other forms of 

leukemia (Harris et al. 1999; Boice 2005; NCI 2009a). Despite its name, it is considered to be a 

form of NHL by the U.S. National Cancer Institute (NCI 2009a) and the World Health 

Organization (Harris et al. 1999). 

The NHLs are a heterogeneous group of lymphoproliferative malignancies with differing 

patterns of behavior and responses to treatment. They can be divided into two prognostic groups: 

the indolent lymphomas and the aggressive lymphomas. Indolent types have a relatively good 

prognosis, with median survival times post-diagnosis as long as 10 years (e.g., most cases of 

CLL), but they are usually not curable in advanced clinical stages. In contrast, the aggressive 

types of NHL have a shorter natural history, but a significant number of patients can be cured 

with intensive combination chemotherapy regimens (NCI 2009a). 

3.2 Origins and Etiology 

CLL is now viewed as a disease originating from antigen-stimulated, mature B lymphocytes, 

which either avoid death through the intercession of external signals or die by apoptosis, only to 

be replenished by proliferating precursor cells (Keating et al. 2003; Stevenson and CaligarisCappio 2004; Chiorazzi et al. 2005; Rai 2007b; Zent 2007; Table 1). 

Although the weight of evidence points to disruption of apoptosis (programmed cell death) 

as an early initiating event in some lymphomas (e.g., Voutsadakis 2000), the genes responsible 

for the origin and progression of the majority of cases of CLL remain unknown (Voutsadakis 

2000; Stilgenbauer et al. 2002; Dewald et al. 2003) and the current view is that an inherent 

apoptotic defect is not the initiating lesion in CLL (Table 1; also see Rai 2007b). 
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Table 1. Comparison of Historical and Current Views of CLL 

Historical view Current viewa

Clinically heterogeneous disease with 

homogeneous cellular origin 

Clinically heterogeneous disease originating 

from B lymphocytes that may differ in 

activation, maturation state, or cellular 

subgroup 

Disease derived from naive B lymphocytes Disease derived from antigen-experienced 

B lymphocytes that differ in level of 

immunoglobulin V-gene mutation 

Leukemic-cell accumulation occurs because of 

inherent apoptotic defect involving entire 

mass of leukemic cells. 

Inherent apoptotic defect involving entire mass 

of leukemic cells is unlikely at the outset. 

Leukemic-cell accumulation occurs 

because of survival signals delivered to 

subgroup of cells from external 

environment through variety of receptors 

(e.g., B-cell receptors and chemokine and 

cytokine receptors) and their cell-bound 

and soluble ligands. 

Disease of accumulation Disease of accumulation with a higher level of 

cell proliferation than previously 

recognized 

Prognostic markers identify patients at various 

risk levels (e.g., low, intermediate, or high 

in Rai staging categories) with 

acknowledged heterogeneity in clinical 

outcomes among patients in low- and 

intermediate risk categories. 

New molecular and protein markers identify 

patients within low- and intermediate risk 

categories who follow different clinical 

courses. 

Therapy is based largely on clinical 

observations and trial-and-error methods. 

New findings provide clues to discrete targets 

for developing hypothesis-driven and 

effective therapeutic agents. a The current view refers to understanding of CLL as it has evolved during the past 10 years 

(Source: Chiorazzi et al. 2005; Rai 2007b). 

The developmental history of CLL (e.g., the stage in B-cell development in which malignant 

transformation occurred) is informative both for predicting the course of the disease and for 

defining the appropriate target organ for dose reconstructions (see Appendix B). 
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CLL cases can be divided into two groups, depending on the degree of somatic 

hypermutation of the immunoglobulin VH genes. Cases from the “unmutated” group (defined as 

having B-CLL cells with VH gene sequences with <2% differences from the germ line cells) 

have a much more aggressive disease course than those from the mutated group, and represent 

about 40% of the cases reported in most Western countries (Chiorazzi et al. 2005; Hamblin 

2008). Interestingly, however, gene-expression profiling studies (Klein et al. 2001; Rosenwald et 

al. 2001) indicate that unmutated CLL cells do not differ from mutated CLL cells in a large 

number of differentially expressed genes, suggesting the possibility of a common origin for both. 

3.3 Characteristics and Relationship to SLL 

CLL results in a clonal expansion of mature-appearing B lymphocytes involving lymph 

nodes and other lymphoid tissues with progressive infiltration of bone marrow and circulation in 

blood (Berkow 1992). The compartments in which CLL proliferates are termed pseudofollicles, 

vaguely nodular areas without mantles that are observed in lymph nodes and bone marrow 

(Stevenson and Caligaris-Cappio 2004). The pathological features of the lymph node in which 

CLL develops are those of a small lymphocytic lymphoma (SLL) (Chiorazzi et al. 2005); SLL 

and CLL are characterized as a single disease entity in the primary extant classification of 

hematopoietic and lymphoid diseases used currently (see Section 4). As lymphocytes 

progressively infiltrate the bone marrow, abnormal hematopoiesis leads to anemia and immune 

dysfunction develops, resulting in increased susceptibility to a variety of infections (Berkow 

1992; Keating et al. 2003). 

As noted earlier, not all cases of CLL are “indolent;” they represent about 60% of the cases 

reported in most Western countries (Chiorazzi et al. 2005; Hamblin 2008). Even within this 

“indolent” group, those that are first diagnosed in later stages (e.g., stage IV in the Rai staging 

system or stage C in the Binet classification system) progress much more rapidly toward a fatal 

outcome, because current therapies are less effective than for other aggressive forms of NHL 

(Keating et al. 2003). 

Patients with indolent (i.e., mutated) CLL (or SLL) often can survive for long periods with 

no or minimal therapy. Because these diseases are often asymptomatic, they are typically 

identified in individuals undergoing routine medical examinations or seeking treatment for other 
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diseases (Berkow 1992). The median survival time for patients dying from B-cell CLL or its 

complications is now about 10 years (Berkow 1992; Keating et al. 2003; NCI 2009b). 

Patients with these forms of lymphoma can die as a result of (1) conversion to a more 

malignant form (such as large B-cell lymphoma or Hodgkin’s disease; Kyasa et al. 2004), (2) an 

increased rate of other malignancies (Kyasa et al. 2004), or (3) complications of treatment, rather 

than from the initially diagnosed form of the disease (Magrath 1992). Those patients who 

experience progression of their CLL predominantly die of complications of the disease itself, 

primarily from infections (Keating et al. 2003). In a study of a well-defined CLL/SLL population 

of predominantly older male smokers, second malignancy was the most common cause of death 

(34%), followed by progressive CLL (18%) and infection (16%) (Kyasa et al. 2004). Because the 

majority of second malignancies in this group were those of the lung and prostate, the 

contribution to mortality from second cancers suggested by these results is undoubtedly an 

overestimate. 

CLL affects older persons primarily, with 75% of CLL patients diagnosed at an average age 

of 60 in the U.S. population. It is about 2−3 times more common in men than in women (Keating 

et al. 2003; Dores et al. 2007; Appendix A). 

3.4 Implications for Assigning a Target Organ for Dose Reconstruction 

The evidence that CLL cells emerge from a population of antigen-selected, mature, 

immunocompetent B-lymphocytes that have carried out typical functions of normal B-cells 

currently seems compelling (Keating et al. 2003; Stevenson and Caligaris-Cappio 2004; 

Chiorazzi et al. 2005; Caligaris-Cappio and Ghia 2007; Ghia et al. 2007; Linet et al. 2007; 

Rai2007b; Appendix B). Activated B cells leave the lymph node (or other site of activation) in 

the efferent lymph, return to the blood and seed the spleen, bone marrow, lymph nodes, mucosal 

associated lymphatic tissue, Peyer’s patches, tonsils, and/or sites of inflammation (Anderson 

2002). Thus, these cells could potentially undergo transformation to CLL clones anywhere in the 

haematopoietic or lymphatic systems. 
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It therefore follows that the bone marrow cannot be designated as the sole target organ for 

induction of CLL by radiation exposure, as is often assumed.1 It would appear that the defining 

lesion for CLL could be initiated anywhere the mature B-lymphocyte precursor might be located, 

i.e., potentially in most tissues of the body, including the bone marrow, but also in the lymph 

nodes, spleen, and mucosa-associated lymphoid tissues.2 Thus, the number of potential target 

organs/tissues needs to be expanded well beyond the three sites (red bone marrow, spleen, or 

thymus) that have been used in estimating PC for CLL (see Section 5.2). 

There are several reasons why the bone marrow cannot be excluded as a potential target 

organ, however. First, after encountering antigen, human equivalents of B-2 cells3 migrate to 

solid lymphoid organs to undergo the germinal center reaction, where they can become memory 

cells, which often migrate to the bone marrow, lying dormant until a repeat antigen exposure 

triggers a similar migration to the lymphoid follicles (Chiorazzi 2007). In addition, some T-celldependent B-cells that become memory or plasma cells via the germinal center reaction migrate 

to the bone marrow where they may persist for many months (Angelin-Duclos et al. 2000) or 

even for a person’s lifetime (McHeyzer-Williams and McHeyzer-Williams 2005). Long-lived

plasma cells preferentially home to the bone marrow (McHeyzer-Williams and McHeyzer-

 

1

 NHL, Hodgkin’s lymphoma, and multiple myeloma also originate from mature B cells (Stevenson et al. 1998), for 

which the bone marrow appears not to be the most appropriate target organ. For example, the precursor cell in 

multiple myeloma is thought to arise from the action of one or more exogenous stimuli while the B-cell is resident in 

a lymph node (Bakkus et al. 1994). The precursor then circulates to the bone marrow where it establishes itself by 

adhering to the stromal cells and inhibiting osteoblastic activity and osteocalcin production (Teoh and Anderson 

1997). 

2

 The immune system operates like a single organ but its lymphatic components are located in every part of the body 

other than the central nervous system. Except for lymphocytes, which are present in most tissues, lymphatic tissue is 

present in the red bone marrow and the thymus (primary lymphoid organs) and the secondary lymphoid organs: 

lymph nodes, spleen, mucus membranes (including tens of thousands of isolated lymphoid follicles in the large and 

small intestines; Brandtzaeg and Johansen 2005; Brandtzaeg et al. 2008), tonsils, adenoids, Peyer’s patches, and the 

vermiform appendix. Integration of this system is effected through lymphocyte recirculation, a process by which 

lymphocytes leave the blood, cross tissues, and return to the blood via efferent lymph. Approximately 1−2% of the 

lymphocyte pool recirculates each hour, optimizing the opportunities for antigen-specific lymphocytes to come into 

contact with antigen in secondary lymphoid tissues (Anderson 2002; Bowers and Hunt 2007). As a result, the total 

circulating pool of lymphocytes in blood is turned over about 50 times a day (Pabst 1988; Westermann and Pabst 

1992). In normal subjects, about 70−80% of blood lymphocytes are T cells and 10−20% are B cells (Westermann 

and Pabst 1992; Mellors 2002). However, T-lymphocytes circulate more readily than B-lymphocytes, whose 

movements are more restricted both inside and outside the confines of the blood and lymphatic systems. Unlike the 

B cells, which tend to remain in lymphoid tissue, T cells constantly recirculate between the peripheral blood and 

lymph in their search for antigen. In addition, the migratory capabilities of specific B lymphocytes may be quite 

different, depending on the B cell subset or lineage from which they derive (Young et al. 1997; Andrade et al. 1998; 

Montoya et al. 1999; Young 1999; Chiorazzi 2007). 

3

 B-1 and B-2 cell lineages have been described in the mouse, but this type of lineage discrimination is not as clear 

in humans (Meffre et al.; 2004; Chiorazzi 2007). 
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Williams 2005), and these authors consider such cells to belong to the memory cell 

compartment.4

Second, an inherited familial predisposition to CLL has been documented (Keating et al. 

2003; Chiorazzi et al. 2005; Linet et al. 2007; Rai 2007a) and genetic differences in 

immunoglobulin genes considered relevant to CLL are observed in populations at low risk for 

developing CLL (e.g., persons of far East Asian ancestry; Keating et al. 2003). Low incidence 

rates for CLL persist in individuals migrating to the U.S. from Asian countries and in their 

descendants (Fig. A.1; Dores et al. 2007; Linet et al. 2007), suggesting that a genetic factor may 

be involved in the reduced levels of CLL observed in Asian populations. Thus, it is conceivable 

that irradiation could produce genetic changes in bone marrow stem cells that could result in 

increased susceptibility to development of CLL that might only be manifested at later stages of 

lymphocyte development. A counter-argument is as follows: One of the reasons CLL appears 

non-radiogenic is that only those (relatively few) persons with an inherited predisposition to 

CLL5

 are affected by radiation exposure, and that radiation acts primarily as a weak promoter, 

e.g., on a B-cell in a later stage of development, rather than as an initiator, acting directly on a 

bone marrow stem cell. Schubauer-Berigan et al. (2007) concluded that a 10-y latency period for 

radiogenic CLL (also see Sect. 5.3) would indicate that radiation acts primarily as a promoter for 

CLL development. Thus, because of current uncertainty about both the lymphocyte precursor for 

CLL and the role of genetic background in induction of radiogenic CLL, the bone marrow should 

be included as one of the potential target organs for development of radiogenic CLL. 

4. CLASSIFICATION AND DIAGNOSIS OF CLL 

As pointed out earlier, diagnostic misclassification is one of the factors that have 

confounded previous attempts to determine whether CLL is radiogenic. Although effective 

diagnosis is now a routine matter for CLL (NCI 2009b), diagnostic classification has long been 

marked by a lack of consistency. Because of the typically old age at onset, many patients die 

 

4

 Rai (2007c) suggested that the B cell leading to CLL was transformed at a stage just prior to that which would 

produce a plasma cell (the last and final stage of a normal lymphocyte’s differentiation). This stage would include 

most memory cells but would rule out the long-lived plasma cells identified by McHeyzer-Williams and McHeyzerWilliams(2005). 5 Potentially associated with mutations in the ATM gene in some cases (see Caligaris-Cappio 2000). 
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with these diseases, but not as a consequence of them. Thus, death certificate information 

provides a very poor indication of the prevalence of CLL/SLL (Richardson et al. 2005). 

B-cell CLL and small lymphocytic lymphoma (SLL) are currently classified as a single 

disease entity in the World Health Organization modification of the Revised European American 

Lymphoma (REAL) Classification (Harris et al. 1999; NCI 2009a). In this classification, 

lymphomas and lymphoid leukemias of the same cell type are considered one disease with 

different clinical presentations or stages, i.e., with the principal distinction between the two being 

the extent of solid tissue or peripheral blood involvement, respectively (Harris et al. 1999; 

Richardson et al. 2005). Put another way, B-cell CLL and SLL may simply be different 

manifestations of the same disease (NCI 2009a). Although CLL/SLL patients in some locations 

may be seen by different physicians based on their presentation [e.g., patients with peripheral 

blood involvement (CLL) are typically seen by hematologists and those with tissue involvement 

(SLL) are seen by oncologists], there is a consensus that the two diseases are biologically the 

same (Harris et al. 1999). 

They are closely affiliated with hairy cell leukemia (HCL), another slowly progressing 

disease of mature B cells. It appears that the prevalence of cases that present as “SLL” may be 

much lower than that of CLL (Dores et al. 2007); it reportedly made up <10% of CLL/SLL cases 

in one major study (Kyasa et al. 2004). All of these forms now fall into the broader category of 

NHL, as noted earlier (Harris et al. 1999; NCI 2009a). 

Although the modified REAL classification has been adopted by the World Health 

Organization and CLL is considered to be an indolent form of NHL by the NCI (2009a), it is not 

the only system in use currently. CLL is classified as a leukemia and both SLL and HCL are 

classified as lymphomas in the International Classification of Diseases (ICD)-9-CM 2001 system 

(AMA 2001). 

Papers on radioepidemiological studies involving CLL referenced by Richardson et al. 

(2005) and Boice (2005) indicate that most of the previous studies (i.e., pre-1996) were 

conducted using the ICD-8 system as the primary classification scheme. However, investigators 

often combined several ICD codes to create their own definitions of CLL or other types of 

leukemia. For example, Weiss et al. (1995) lumped CLL per se (i.e., as then defined to include 

diseases of both B- and T-cell origin; ICD-9 code 204.1) with unspecified lymphocytic 

leukemias (ICD-9 code 207.8), while HCL (a lymphoma in the ICD-9 scheme; code 202.4) was 
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lumped with acute myeloid leukemia. Epidemiological studies of the Japanese atomic-bomb 

survivors have utilized previous iterations of the ICD classification system (Preston et al. 1994; 

Thompson et al. 1994), and the most recent studies were based on the ICD-9 system (Preston et 

al. 2004). Other chronic leukemic patterns that have been categorized as “CLL” in clinical 

practice include: prolymphocytic leukemia, HCL, lymphoma leukemia, and Sézary syndrome 

(Berkow 1992). Thus, the effects of changing definitions, which likely increased the potential for 

misdiagnoses or mischaracterizations of CLL, must be carefully considered in interpreting the 

results of epidemiological studies involving CLL.6 

5. DEVELOPMENT OF A RISK MODEL FOR CLL 

5.1 Selection of Risk Coefficients 

Based on the information reviewed to date, and on the risk models currently developed for 

IREP, the risk model in IREP for NHL, Hodgkin’s disease (lymphoma), and multiple myeloma 

(lymphoma and multiple myeloma grouping; ICD-9 codes 200−203) (Preston et al. 1994; Land 

et al. 2003) could serve as the basis for a CLL risk model. This model is based on the incidence 

of 117 cancers of these types observed through the year 1987 in the Japanese A-bomb survivors 

exposed to doses ≥0.01 Sv: 76 cases of NHL, 10 cases of Hodgkin’s lymphoma, and 31 cases of 

multiple myeloma (Preston et al. 1994; UNSCEAR 2008; Land et al. 2003; Table 2). 

Although the risk model in IREP is based on data for all three types of cancers, UNSCEAR 

has derived risk estimates for each cancer type individually (Table 2). Point estimates for the 

ERRs for males for NHL and multiple myeloma were positive, while those for females were 

negative. Data for Hodgkin’s lymphoma were not extensive enough to attempt analyses by 

gender or age at exposure. 

The central value of the point estimate of the ERR for males in the model for the lymphoma 

and multiple myeloma grouping used in IREP was also positive, while that for females was 

 

6

 A T-cell form of CLL reportedly made up about 5% of CLL cases diagnosed in the past. Although T-cell CLL and 

prolymphocytic leukemia are grouped together under peripheral T-cell and NK-cell neoplasms in the updated 

REAL/WHO classification, it appears that CLL is currently considered a disease of exclusively B-cell origin (see 

Chiorazzi et al. 2005; Chiorazzi 2007; Rai 2007b, Rai 2007c; Zent 2007; NCI 2009a; NCI 2009b). Further, Rai 

(2007b) indicates that the ratio of T cells to B cells in peripheral blood is reversed in CLL, with over 90% of 

circulating lymphocytes being B-CLL cells. 
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Table 2. Risk estimates for incidence of non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, 

and multiple myeloma in the Japanese atomic-bomb survivors at doses ≥0.01 Sv

Cancer site Internal grouping 

Number 

of cases 

Average ERR 

at 1 Sva Source 

Non-Hodgkin’s 

 lymphoma 

Gender Male 41 0.44 (−0.16, 1.42) Table 41 in 

 UNSCEAR (2008), 

 based on data in 

 Preston et al. 

 (1994) 

Female 35 −0.22 (<−0.22, 0.40) 

Age at 

 exposure 

<20 y 17 0.45 (<0, 2.16) 

20−40 y 34 −0.12 (<−0.12, 0.73) 

>40y 25 0.09 (<0, 1.04) 

Time 

since 

exposure 

12−15 y 7 0.33 (<0, 2.14) 

15−30 y 34 0.33 (<0, 1.44) 

>30 y 35 −0.22 (<−0.22, 0.45) 

All 76 0.08 (<0, 0.62) 

Hodgkin’s 

 lymphoma 

All 10 0.43 (-1.6, 3.5) Table 42 in 

 UNSCEAR (2008), 

 based on data in 

 Preston et al. 

 (1994) 

Multiple 

 myeloma 

Gender Male 12 0.17 Table 43 in 

 UNSCEAR (2008), 

 based on data in 

 Preston et al. 

 (1994) 

Female 18 −0.28 

Age at 

 exposure 

<20 y 4 1.07 

>20y 26 0.09 

All 30 0.20 (<-0.2, 1.7) 

Multiple 

 myeloma 

All 31b 0.26 (0, 1.85)c Land (2000) 

a

90% C.I. in parentheses, except where noted. 

b

Includes one additional case of multiple myeloma not originally included in the analysis by 

Preston et al. (1994). c

95% C.I. in parentheses. 

 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 16 

negative. For the IREP model, it was assumed that the ERRs for the two sexes were the same, 

although there reportedly was suggestive evidence that they differed (p = 0.09). The age 

parameters used were derived from an analysis of the incidence of solid cancers other than 

digestive cancers, female stomach cancer, liver cancer, female breast cancer, thyroid cancer, and 

non-melanoma skin cancer. The common age parameters derived from this analysis were 

reportedly used for the lymphoma and multiple myeloma model because there was little evidence 

of departure from them in the grouped data for the latter (Land et al. 2003). 

The risk model for lymphoma and multiple myeloma in IREP is defined as follows: 

• For exposure ages e ≥30 and attained ages a ≥50, the ERR at 1 Sv = 0.178 (95% C.I. <0, 

0.9465); 

• For exposure ages e <30 and attained ages a <50, the ERR at 1 Sv is multiplied by a modifier 

with a geometric mean = exp{−0.05255 f(e) −1.626 g(a)}, and a geometric standard 

deviation = exp{[0.0003261 f(e)

2 −0.014594 f(e) g(a) + 0.5648 g(a)2

]

1⁄2}, where 

3⁄4 f(e) = −15 for ages at exposure ≤15, increasing monotonically as (e-30) from the value 

at age 15 to a value of 0 at age 30; after age at exposure 30, it remains equal to zero. 

3⁄4 g(a) = ln(a/50) for attained ages <50, and 0 for attained ages ≥50. 

The risk models in NIOSH-IREP are not used to perform risk calculations for persons who are 

<15 y old. The variation of ERR at 1 Sv with attained age is shown in Fig. 1 for selected ages at 

time of exposure. 

For low acute and fractionated exposures and for chronic exposures, the dose and dose rate 

reduction factor (DDREF) for solid tumors is applied to these risk estimates. The DDREF is 

phased in at doses <0.2 Gy for acute and fractionated acute exposures (Land et al. 2003). 

5.2 Concerns Associated with Use of the Lymphoma and Multiple Myeloma Model 

Application of the lymphoma and multiple myeloma risk model as the basis for CLL is 

subject to a number of issues and caveats. The first issue is whether a risk model for CLL should 

be based on data for NHL alone, i.e., by reanalyzing the data after excluding Hodgkins 

lymphoma and multiple myeloma. There are major differences in etiology and effects of 

Hodgkins lymphoma and multiple myeloma compared to NHL (NCI 2009a). One potential 

argument in favor of inclusion of multiple myeloma is that, like CLL, it too is a disease of old 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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0.0

0.5

1.0

1.5

0 10 20 30 40 50 60 70 80 90 100

Age at exposure 18

Age at exposure 25

Age at exposure 30

ERR/Sv

Attained Age (years)

Fig. 1. Variation of the ERR at 1 Sv with attained age for selected ages at exposure in the 

lymphoma and multiple myeloma model in IREP, which is used as a surrogate for a risk model 

for CLL in this report. 

age (Preston et al. 1994), and some aspects of its causation may be similar to some types of CLL

 (Lonial 2005). However, the median survival time for patients diagnosed with multiple 

myeloma is about three years (George and Sadovsky 1999), as compared to ~10 years for CLL. 

Arguments for inclusion of Hodgkins lymphoma seem less compelling, even though the 

estimated median survival post-diagnosis is about the same as that for CLL (~10 y; Grund 2004; 

NCI 2009c). 

Use of data for NHL alone reportedly results in a similarly low risk coefficient, comparable 

to that for the grouping, but with somewhat tighter confidence limits than if the other two 

diseases are included (see Table 2 and text describing the model for the lymphoma and multiple 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 18 

myeloma grouping). One concern is that, although both indolent and aggressive forms of 

lymphoma are included in the definition of NHL, when CLL is excluded (as in the Japanese 

atomic bomb survivor data) more cases of NHL may fall into the “aggressive” category than for 

CLL or for an NHL grouping that includes CLL (NCI 2009a, 2009b). Another is that the 

grouping of cancers in the current “NHL” data set is incomplete. It excludes, in addition to CLL, 

HCL, and possibly other conditions (discussed earlier) that are included in the current definition 

of NHL by the NCI (2009a). These types of cancers are included in a grouping called “other 

leukemias” by Preston et al. (2004) (see Appendix B). 

Thus, the possibility exists that even NHL as defined in the questionable ICD classification 

system used in analyses of dose-responses in the LSS cohort might not be a good surrogate for 

CLL. However, since some, perhaps many, cases of CLL in former nuclear weapons workers 

covered under the EEOICPA were undoubtedly diagnosed in the past using a variety of 

classification schemes, use of a blanket category such as NHL (or the entire lymphoma plus 

multiple myeloma grouping) to develop a risk model for CLL may be the only interim option at 

present. 

Killing of highly radiosensitive B-lymphocytes in persons who received acute doses ≥1 Gy 

(e.g., some members of the LSS cohort and medically exposed groups) (Mettler and Upton 1995; 

Waselenko et al. 2004) would have significantly reduced the pool of CLL (and NHL) precursors 

in which malignancies could have been initiated, resulting in a potential depression of the doseresponse as a “cell-killing” effect. The ERR/Sv reported by Richardson et al. (2009) for NHL 

mortality in the LSS cohort when survivor doses were limited to <0.5 Sv (4.24; 90% C.I. 0.83, 

9.76) was about four times greater than the ERR/Sv based on the full dose range (1.12; 90% C.I. 

0.26, 2.51). These results are suggestive of an effect of cell-killing at higher doses. Intriguingly, 

Schubauer-Berigan (2007) obtained a marginally significant dose-response (ERR/10 mSv: 0.20; 

95% C.I. -0.035, 0.96) in a study of mortality from CLL in a case-control study involving 

workers from six U.S. nuclear facilities when workers with doses >0.1 Sv were excluded. 

However, this observation could simply be a result of data selection and/or the small numbers of 

individuals with higher doses. 

Development of a completely new risk model, e.g., based solely on incidence of NHL or 

some plausible combination of NHL with a lymphoma considered similar to CLL, such as HCL, 

that has not heretofore been included in the NHL category in previous analyses of data for the 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 19 

LSS cohort, would require: (1) full access to relevant DS02 dose-response data currently being 

analyzed by the Radiation Effects Research Foundation (RERF), (2) an independent Poisson 

regression analysis of these data leading to a unique risk model and associated parameter set, and 

(3) access to a specialized software package (e.g., AMFIT module of the software package 

EPICURE; Preston et al. 1998) for such an analysis. Alternatively, the NCI staff members who 

developed the current risk models in IREP could be asked to perform the required analyses. One 

obvious disadvantage of performing such analyses at the current time is that extended 

information on the LSS cohort, i.e., dose responses for lymphoma and leukemia incidence based 

on the new DS02 dosimetry system are yet to be published and thus are not generally available. 

Another issue is that the risk model in IREP was based on Japanese atomic-bomb survivor 

data that were analyzed assuming that the bone marrow dose was the appropriate reference 

(Preston et al. 1994). However, as discussed above, newer information on the origins of all of the 

diseases covered by the lymphoma plus multiple myeloma grouping indicate that use of a bone 

marrow dose is not appropriate, and that a whole-body dose should be more representative of the 

exposure conditions of the precursors of these diseases if induced under conditions of uniform 

external exposure (as in the LSS cohort). We would expect that, all else being equal, use of a 

bone marrow dose could yield somewhat lower risk coefficients than use of a whole-body dose 

(perhaps ~5% lower; see dose adjustment factors given in Walsh et al. 2004). 

When and if these data are reanalyzed in future (i.e., after DS02-based dose-response 

information is made available), an appropriate surrogate for a whole-body dose should be used 

instead of the bone marrow dose alone, e.g., perhaps by an appropriate combination of the 

esophageal, colon, and bone marrow doses, because most of the lymphatic system, like the 

esophagus, lies close to the surface of the body, while the circulating blood is more uniformly 

distributed the throughout the tissue volume. An alternative to a whole-body dose that could 

provide a more representative dose to the potential lymphocyte precursors of CLL would be a 

weighted average. It could be estimated by applying relative weights to doses to the different 

lymphocyte-containing organs/tissues based on distributions of lymphocytes in the human body 

(e.g., Trepel 1974; Ganusov and De Boer 2007; Blum and Pabst 2007). Unfortunately, however, 

these distributions, which are based partly on extrapolations of data on laboratory animals 

(mainly rodents) to humans, do not provide information for the individual categories of 

lymphocytes. This is problematic because T lymphocytes outnumber B lymphocytes by about 2 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 20 

to 1 in the human body as a whole (Westermann and Pabst 1992; Young 1999; Mellors 2002), 

and constitute >95% of the lymphocytes in some tissues and organs (Westermann and Pabst 

1992). 

Because lymphocytes, as well as the lymphatic organs and tissues, are distributed widely in 

the human body, but the exact proportions of B-lymphocyte precursors for CLL (or ratios of B 

cells to T cells) within any of the regions are not known precisely, making such fine distinctions 

does not appear to be justified when estimating a whole-body dose at the present time (Section 6; 

also see Apostoaei and Trabalka 2009).7 

5.3 Handling of Risk Transfer between Populations 

Because of the very large difference in baseline risks for CLL between the Japanese A-bomb 

survivors and the U.S. population (Appendix A), the issue of risk transfer also required special 

consideration. The risk transfer refers to whether a multiplicative or additive risk model for the 

Japanese population should be given higher weight when applied to the U.S. population. In 

IREP, equal weights are given to these models (Land et al. 2003). 

One could argue, for example, that if the interaction between CLL and radiation was more 

multiplicative than additive, a higher incidence might have been observed in epidemiological 

studies of radiation workers and medically exposed groups. However, the NIOSH review of 

epidemiological studies related to CLL (Silver et al. 2007) reinforces the point that the follow-up 

periods in such studies were probably too limited to permit detection of a dose-response for CLL 

because of an extended latency period (see Fig. 2). In addition, most studies were focused on 

mortality rather than incidence as an endpoint, which, as noted earlier, may have significantly 

underestimated the occurrence of CLL. 

The potentially weak radiogenicity of CLL, combined with inadequate follow-up, 

inconsistent diagnosis and classification of CLL, and a questionable endpoint (mortality) in 

 

7

 The distribution of lymphocytes in the human body given in ICRP Report 89 (ICRP 2002): 0.2% in blood, 7% in 

red marrow, 7% in lymphatic tissues, and about 86% elsewhere, suggests that most lymphocytes are located outside 

the bone marrow, circulatory, and lymphatic systems at any given time. However, this information was based on a 

now-discredited reference (see Trepel 1974). A more credible distribution is as follows: 2% in blood, 10% in red 

marrow, 80+% in lymphatic tissues, and <8% in other tissues (Blum and Pabst 2006). In addition, much of the 

information on the lymph nodes in the 2002 ICRP report is derived from outdated material (dates of references 

ranging from 1885−1926) originally included in its 1975 report on reference man (ICRP Publication 23). 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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Fig. 2. Results from medical cohort studies and clinical trials suggesting a latency for incidence 

of radiogenic CLL of about 15 y or more, based on an analysis of mean follow-up time. Outcome 

is CLL mortality, except where noted. aIncidence studies. bRisk estimate for patients who 

received radiotherapy was lower than for patients who were not irradiated. Source: Silver et al. 

(2007). 

 

epidemiological studies, appears to make any judgment about the relative contributions of 

alternative representations of excess radiogenic risk inappropriate. Thus, it is concluded that the 

“uninformed” risk transfer module (as described on page 35 and in Fig. IV.G.1 of Land et al. 

2003), which is used for the bulk of solid tumors in IREP, as well as lymphoma and multiple 

myeloma, is a more appropriate choice for the CLL model, based on current information. 

5.4 Latency 

Our review of the epidemiological data indicated that the extended latency period (~20−30 

years) for mortality from CLL suggested by Richardson et al. (2005) would not be applicable to 

a risk model for incidence of CLL (i.e., as in IREP). One reason is that the post-diagnosis 

survival times for CLL patients are highly uncertain, potentially ranging from a few months to a 

normal life expectancy (NCI 2008b). Another is that it is questionable whether existing 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 22 

epidemiological data on CLL induction by radiation are capable of providing useful information 

on an extended latency period (Appendix C). 

Beyond the obvious concerns about potential for misdiagnoses and classification of CLL, all 

of the studies of medically exposed persons that we examined are potentially limited by the (1) 

confounding effects of prior disease on CLL incidence, (2) uncertainties in diagnosis and 

classification of CLL, (3) limited statistical power, particularly in one study of the consequences 

of diagnostic or therapeutic uses of 131I for thyroid diseases because of the relatively low doses 

involved, and, to a lesser extent, (4) meaningfulness of bone marrow doses for lymphoma 

induction. 

If we subtract an estimated median post-diagnosis survival time of ~10 y from the estimate 

of Richardson et al., the resulting estimate of latency of ~15 y for incidence of CLL is consistent 

with that suggested by the analysis of Silver et al. (2007) based on an examination of mean 

follow-up time in medical cohort studies, in which the risk of CLL “is elevated, though nonsignificantly, in almost all studies with more than 15 years average follow-up” (see Fig. 2) and 

our own admittedly crude point estimate (see Appendix C). 

However, recent time window analyses within epidemiological studies highlight the 

uncertainty in potential latency periods for both CLL and NHL. A case-control study of mortality 

from CLL in occupationally exposed persons by Schubauer-Berigan et al. (2007) yielded 

marginally significant estimates of risk with a time lag of ≥10 years when workers with doses 

>100 mSv were excluded, suggesting that a lag period of 10 years was appropriate for this group. 

The study by Romanenko et al. (2008), which did not examine the effects of the very large 

uncertainty in doses for Chornobyl remediation workers (see Kryuchkov et al. 2009), reported a 

minimum deviance for the risk estimate for both CLL and non-CLL leukemia using a time lag of 

only 2 years. In contrast, although Richardson et al. (2009) obtained significant dose-responses 

(p ≤0.05) for mortality from NHL in both the LSS cohort and workers at the Savannah River Site 

using a time lag of 10 years, their study did not show evidence of a significant risk until a period 

of 35+ years had elapsed following irradiation. “In each cohort, evidence of a dose-response 

association was primarily observed more than 35 years after irradiation. Such findings indicate a 

protracted induction and latency period” (Richardson et al. 2009). Such contrasting results 

indicate that the issue of a suitable estimate of latency for modeling risks of radiogenic CLL or 

NHL is far from resolved. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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The results of our evaluation of information from medical cohort studies bearing on latency 

of multiple myeloma and NHL are similar to those for CLL (Appendix C), i.e., an uncertainty 

band of ±5 y around a point estimate of latency for CLL or NHL of ~10−15 y. Because of 

current uncertainty in the value for the central estimate of a latency period, we have elected to 

use a point estimate of 10 y with an uncertainty band of ±5 y. The characteristics of the resulting 

latency distribution are described in the next section. 

5.4.1 Handling of Latency in the CLL Model 

As with other neoplasms in the IREP model, the risk of CLL should be very low for short 

times after exposure due the existence of a minimum latency period (here assumed to be 5 y). 

For longer times after exposure the magnitude of risk is adjusted by a factor which is phased in 

using an S-shaped function, as described on pages 95−96 of Land et al. (2003). For the CLL risk 

model, the midpoint of the S-shaped function for latency was set at 10 y. At the midpoint, the 

ERR/Sv is half the maximum ERR/Sv, which is attained at about 20 y. Figure 3 shows the ERR 

at 1 Sv for CLL adjusted by an S-shaped function with a mid-point of 10 y, to account for the 

effect of the minimum latency period. 

Because precise knowledge about the onset of radiogenic CLL is lacking, an additional 

random linear translation of the S-shaped curve is introduced by letting the midpoint vary around 

the nominal value of 10 y. The uncertain midpoint is assumed to be distributed according to a 

triangular distribution with minimum 5 y, mode 10 y, and maximum 15 y. The effect of this 

uncertainty is an increase of assigned share for malignancies diagnosed within a shorter time 

following exposure (Fig. 4). 

The midpoint of the S-shaped latency function used in IREP for the bulk of solid tumors, 

including the lymphoma and multiple myeloma grouping, is 7.5 years, and reaches 0.01 and 0.99 

at 4 y and 11 y, respectively. The addition of a triangular distribution (with extremes at 5 and 10 

years) about the midpoint extends the points at which the 99th percentile of the latency 

adjustment reaches 0.01 and 0.99 to approximately 1.5 y and 9 y, respectively (Land et al. 2003). 

In contrast, the latency adjustment represented by the central curve in Fig. 4 reaches 0.99 of 

the maximum at 17 y after exposure. When the uncertainty in the mid-point of the S-shaped 

function is included, the point at which the 99th percentile of the latency adjustment reaches 0.99 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 24 

0.0

0.5

1.0

1.5

0 10 20 30 40 50 60 70 80 90 100

Age at exposure18

Age at exposure 25

Age at exposure 30

ERR/Sv

Attained Age (years)

Fig. 3. Risk model for CLL, adjusted for the effect of minimum latency period, depicted as the 

variation of the ERR at 1 Sv with attained age, for ages selected ages at exposure. 

is extended to 12-13 y. As a result, the 99th percentile of PC for CLL should be based on values 

for latency of about 12-13 years. 

The differences between the revised latency adjustment proposed for the CLL risk model 

and the existing latency adjustment in IREP are introduced by the assumption of a slightly longer 

but more uncertain minimum latency period (10 y with a range of 5 to 15 y for the CLL model 

versus 7.5 y with a range of 5 to 10 y used in IREP). Despite the differences in the two 

distributions, 99th percentile estimates of PC made using the revised distribution should be rather 

similar to those made with the existing latency distribution in IREP, all else being equal, with the 

revised distribution expected to produce lower 99th percentiles of PC at 5 to 20 y after exposure, 

but possibly producing higher 99th percentile of PC at 2−5 years after exposure—the latter effect 

being caused by the larger uncertainty in the revised latency adjustment proposed for the CLL 

model. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30

Latency adjustment (unitless)

Time after exposure (years)

CLL

Fig.4. Adjustment factor accounting for the uncertainty in the minimum latency period. 

5.5 Sample Results from the Proposed CLL Model 

Representative estimates of PC from the CLL model are given in Table 3 for an acute 

exposure of 1 Sv to high-energy gamma radiation. Although results are restricted to exposures to 

males, the 99th percentile of PC for females is similar to that obtained for males because the same 

risk coefficients are used and because the upper bound of PC is given by the multiplicative risk 

projection to the U.S. population, provided that the Japan to U.S ratios of age-adjusted incidence 

rates in males and females are significantly lower than 1 (Appendix A). As shown in Table 3, the 

PC exceeds 50% at the 99th percentile for times since exposure greater than 10 y for men aged 20 

y at the time of exposure, and for times since exposure between 10 and 20 y for men aged 25 y at 

the time of exposure. For age at exposure 20, the 95th percentile of PC also exceeds 50% for 

cancers diagnosed 15 and 20 y after exposure. Doses higher than 1 Sv will be required to 

produce 99th percentile values of PC that equal or exceed a value of 50% for older ages at time of 

exposure or for older ages at time of diagnosis when age at exposure is greater or equal to 25 y. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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Table 3. Probability of causation (PC in %) of CLL in males acutely exposed 

to 1 Sv of high-energy gamma radiation 

Age at 

exposure 

(y) 

PCa 

(percentile) 

Time since exposure (y) 

5 10 15 20 25 ≥30 

20 

50th 0.80 7.84 13.8 12.2 10.4 8.88 

95th 16.7 48.4 54.2 50.1 45.4 41.3 

99th 35.0 65.0 67.6 63.2 58.6 54.6 

25 

50th 0.46 4.88 9.07 8.16 6.99 6.99 

95th 9.91 35.4 41.9 38.7 34.8 34.8 

99th 22.5 51.0 55.3 51.3 47.2 47.2 

30 

50th 0.28 3.09 5.98 5.48 5.49 5.49 

95th 6.04 24.9 31.2 28.9 28.9 28.9 

99th 14.4 38.3 43.3 40.1 40.2 40.2 

35 

50th 0.23 2.56 5.10 5.48 5.49 5.49 

95th 4.85 21.2 27.5 28.9 28.9 28.9 

99th 11.5 33.4 38.6 40.1 40.2 40.2 

40 

50th 0.19 2.16 5.10 5.48 5.49 5.49 

95th 4.01 18.4 27.5 28.9 28.9 28.9 

99th 9.50 29.5 38.6 40.1 40.2 40.2 

a

 PC were obtained using a modified version of NIOSH-IREP v.5.6 in which the "Lymphoma and Multiple 

Myeloma (200-203)" model was adapted to represent CLL. The PC values were obtained based on 30 runs with 

2000 iterations each. 

For testing purposes, the proposed CLL model was also applied to seventeen claimants 

selected by NIOSH/OCAS (Development of a CLL Risk Model for NIOSH-IREP − Revised 

Addendum, dated December 10, 2009). These claimants had diseases with ICD-9 codes 202.2x 

(Sezary's disease) or 202.4x (hairy cell leukemia), which have been described in the literature as 

clinically resembling CLL. The PC values at the 99th percentile obtained using the proposed CLL 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 27 

model were compared to the PC values at the 99th percentile obtained using the "Lymphoma and 

Multiple Myeloma (200-203)" model in NIOSH-IREP v.5.6. All PC values for these claimants 

were lower than 50% regardless of the model used. The PC values produced by the proposed 

CLL model are always lower (sometimes significantly lower) than the values obtained using the 

current Lymphoma and Multiple Myeloma model in NIOSH-IREP. The reason for the lower PC 

values at the 99th percentile is the longer latency used in the proposed CLL model. 

6. DOSE RECONSTRUCTIONS FOR NUCLEAR ENERGY WORKERS 

FOR USE WITH THE CLL MODEL 

6.1 Background 

As discussed earlier, estimates of external doses for use in modeling the radiological risk of 

CLL appear to be well represented by whole-body doses, but not by bone marrow doses. For 

purposes of dose reconstruction made in conjunction with risk estimates based on IREP, 

information from existing personal dosimeter or film badge records for doses from penetrating 

radiation should probably suffice in most instances. 

For internal exposures or for non-uniform external exposures to former radiation workers, 

however, the situation is much more complex. The target organ or tissue is the irradiated site in 

which a normal stem or stem-like cell undergoes transformation to a cancerous clone. The 

leukemias per se (as distinguished from diseases such as CLL and HCL) are monoclonal diseases 

that arise from hematopoietic stem and progenitor cells located in the red bone marrow. Thus, 

the red bone marrow is considered both the sole target organ for radiogenic leukemias and the 

site in which the pathological effects of the leukemic clones develop. 

For CLL, the precursor cell is a mature, likely antigen-experienced lymphocyte of currently 

uncertain lineage or subtype (among the known subsets of normal B cells) and of unknown 

location in the body when it was transformed to a B-CLL cell (by radiation or some other 

stimulus). Thus, the appropriate target organ is unclear (Schubauer-Berigan et al. 2007). The 

residence times of potential lymphocyte precursors at given locations within the body prior to 

transformation, which normally would be used to estimate the potential dose received, and their 

effective lifespan, which should limit the time during which they could have been irradiated, are 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 28 

also essentially unknowable. Under these conditions, the risk from radiation exposure cannot be 

estimated from radiation doses reconstructed using a conventional “target organ” approach. 

Questions that immediately arise include: If the dose to a mature B lymphocyte determines 

the biological response to radiation exposure, but that B cell potentially could have arisen at any 

time during the life of an individual diagnosed with CLL, does the term “radiation dose” as it is 

normally used in conjunction with a specific population of cells within a target organ or tissue 

have any meaning? How does one define “age at exposure” or “time since exposure” under such 

conditions? To what extent does irradiation of stem and progenitor cells of the lymphocytes in 

the bone marrow influence later development of CLL, even though the event that transforms the 

normal B cell to a CLL clone occurs at a later time and most probably in a different location? Is 

it possible to develop an aggregated estimate of internal dose incorporating appropriate 

uncertainties to cover the range of behaviors, locales inhabited, residence times, and lifespan 

typical of the half-dozen types of mature B cells that are currently considered to be potential 

CLL precursors? To what extent does such an estimate have to be tailored to account for the 

route of entry of a radionuclide and internal kinetics and partitioning specific to a given 

radionuclide? Given the paucity of relevant information, would such an aggregated estimate be a 

case of “garbage in,” “garbage out?” 

Under ideal conditions, dose estimates would be made considering the potential routes of 

entry (e.g., inhalation vs wounds) and radionuclide-specific patterns of transport and 

accumulation. For inhaled plutonium, for example, highest doses might accrue to the lymph 

nodes associated with the lung. However, transfers via the circulatory system to other organs and 

tissues, including the bone marrow, from the lung, extrathoracic regions of the respiratory tract, 

and their associated lymph nodes will result in additional doses to circulating lymphocytes while 

in blood and to circulating and sedentary lymphocytes while transiting or residing in the bone 

marrow and lymphatic system. 

Since a fraction of B lymphocytes spend some of their time outside the blood, lymph, bone 

marrow, and lymphatic tissues per se, plutonium taken up by organs and tissues such as liver and 

skeleton could represent another important source of exposure. For example, the liver contains a 

large resident and migratory population of lymphocytes and macrophages that provide immune 

surveillance against foreign antigen (Lalor and Adams 2002). Unfortunately, the exact 

proportions of the pools of the half-dozen CLL precursors—or of the total pool of B 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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 29 

lymphocytes—in specific regions of the human body, such as the liver, are not currently known. 

Since mass distributions of lymphocytes are based primarily on information that does not 

distinguish among B, T, or natural killer cells, and as such are dominated by the more abundant 

T cells, performing a mass balance for B cells carries with it high uncertainty and limits the use 

of data in some of the published sources (e.g., ICRP 1994, 2002). 

The situation for CLL is analogous to that for NHL, in which radiation could have interacted 

with the lymphocyte precursors anywhere in the lymphatic or circulatory systems, and then 

formed the cancerous lesion elsewhere. Because the site of origin for NHL could not be 

determined with any confidence—and data on the inventories and distributions of lymphocytes 

in the human body are quite uncertain, the dose to the organ/tissue receiving the highest dose is 

used in dose reconstructions for NHL (ORAU 2006). For both B-cell and T-cell lymphomas, the 

thoracic lymph nodes were selected as the target organ because the dose to these tissues from 

exposure via inhalation of insoluble radioactive material is always higher than the dose to other 

organs/tissues. Thus, a similar approach could be applied to CLL if a more scientifically 

acceptable alternative cannot be developed. 

For radionuclides that are more uniformly distributed in the body, such as tritium and 137Cs, 

estimates of whole-body doses based on concentrations and the effective half-lives of the 

radioactive materials should be sufficient. For other radionuclides with non-uniform deposition 

patterns in the body (e.g., 131I), doses to components of the lymphatic system and to the 

circulating blood would have to be estimated on a case-by-case basis, as for plutonium. 

How to aggregate doses to individual components of the lymphatic and circulatory systems 

for purposes of developing the input to IREP is an issue that would have to be evaluated 

carefully. One potential way to accomplish this is to estimate dose rates to circulating 

lymphocytes based on the estimated fraction of the time that they spend while in blood or lymph 

and in tissues outside and inside the lymphatic and hematopoietic systems, e.g., based on 

residence times of and dose rates from radionuclides within these compartments or on dose rates 

to lymphatic components or bone marrow from nearby organs of accumulation (such as the 

thyroid, which can irradiate nearby lymph nodes following uptake of 131I).8

 

 

8

 Lymphocytes as a group spend about 0.5 h in the bloodstream, after which ~45% move into the spleen, where they 

spend several h, ~42% get into the lymph nodes, where they spend about 12 h, and ~10% cross the endothelial cells 

lining the capillaries and move into other body tissues. Details on the fractional distributions of B, T, and natural 

killer lymphocytes in each of these fluxes are currently lacking. 
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ICRP dose models all contain a blood compartment that can be used to calculate dose rates 

for lymphocytes in circulating blood and organs and tissues of accumulation, but it appears that 

information on transfers to the lymphatic compartments is very limited, except for inhaled 

materials, for which information on transfers to the lymph nodes associated with the lungs and 

extrathoracic regions of the respiratory tract is available (ICRP 1994). Thus, obtaining 

meaningful dose estimates to use as input for IREP for former radiation workers could be a 

significant challenge, depending on the nature of the exposure and the specific radionuclides 

involved. 

If, as expected, this challenge proves to too daunting for some combinations of exposure 

type and radionuclide, an alternative would be to estimate the maximum dose to a lymphocyte in 

any component of the circulatory, lymphatic, or blood-forming systems or in any other organ or 

tissue through which they circulate or in which they reside—as is currently done using the 

thoracic lymph nodes as the target organ for NHL. However, there are also significant challenges 

associated with making such an estimate and serious questions about whether use of such an 

estimate would even be justified—particularly in the case of CLL, for which an association with 

radiation exposure is still in doubt. We will use the example of inhaled plutonium again to 

illustrate the associated technical questions and concerns. 

6.2 Challenges in Estimating Meaningful Doses for Use with the IREP CLL Model 

Cells in the thoracic lymph nodes are expected to experience the highest dose rate following 

inhalation of plutonium (ICRP 1994). Under these conditions, is it reasonable to use a 

conservative estimate of dose referenced to the thoracic lymph nodes to estimate the probability 

of causation of CLL using IREP? The argument in favor of this approach is that uncertainties 

about the nature of the CLL precursor, including its homing/migratory behavior, cannot rule out 

the possibility that such a cell could have spent most of its lifetime in the thoracic lymph nodes. 

If one gives the benefit of the doubt to the claimant, as has been done for other B-cell 

lymphomas (ORAU 2006), shouldn’t the organ potentially receiving the highest dose be the 

target organ for dose reconstruction? 

The counter-argument is that a conservative estimate may be difficult to justify on technical 

grounds, given the weak evidence for radiogenicity of CLL. Even if a lymphocyte is periodically 
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irradiated by α particles in these lymph nodes at a high dose rate while it is passing through or 

temporarily resident in the lymph node, the probability that it will receive a significant dose from 

plutonium could be quite low because some lymphocytes can spend a significant part of their 

time outside the lymphatic system (e.g., while circulating in blood) or could be localized for long 

periods in lymphoid tissues remote from the thoracic lymph nodes. 

As an alternative to an assumption of total residence in the thoracic lymph nodes, one could 

attempt to estimate a dose to a lymphocyte precursor for CLL based on the ratio of the mass 

(volume) of the thoracic lymph nodes to those of the circulating blood, lymphatic system, bone 

marrow, or the body mass through which lymphocytes circulate. The mass of the thoracic lymph 

nodes in adults (15 g) is about 2−2.5% of that of the mass of the lymphatic system as a whole 

(600−730 g, depending on gender) and about 0.025−0.04% of the lean body mass (ICRP 1994, 

2002). Thus, the probability that a lymphocyte might be in the thoracic lymph nodes at any given 

point in time could range from about 2% for the hypothetical case of a B-lymphocyte restricted 

to but circulating within the lymphatic system alone and as low as about 0.03% for the highly 

unlikely case of a B-lymphocyte circulating freely into and out of extra-lymphoid organs and 

tissues. 

In the case of a B-lymphocyte able to circulate freely through only the blood and lymphatic 

systems (e.g., antigen-naïve mature B cells, some types of memory cells), the probability would 

be reduced by about an order of magnitude, i.e., from about 2% to about 0.2−0.3%, when 

compared to that of a B-lymphocyte restricted to the lymphatic system alone because of the extra 

volume (and mass) contributed by the blood (4.50−6.03 L; specific gravity about 1.05; ICRP 

2002).9 Multiplying these probabilities by the expected dose rate in the thoracic lymph nodes and 

the estimated lifetime of the lymphocyte precursors for CLL could yield an estimate of dose 

because some lymphocytes spend only part of their time at any one place, either inside or outside 

the lymphatic system. 

However, as previously discussed, there are large uncertainties involving the 

characterization of the lymphocyte precursor for CLL/SLL, the degree to which precursors 

circulate or are localized in the blood and lymphatic systems or other organs and tissues at the 

point in time when they undergo transformation to become CLL clones, and the nature of post-

 

9

 A large fraction of B lymphocytes in the peripheral blood do not recirculate, but tend to remain in the blood 

(Young 1999). Thus, the cases described are oversimplifications of what actually happens in the human body. 
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transformation transfers of the clones to regions where the disease manifests itself (e.g., bone 

marrow, lymphatic components, or other organs and tissues). All of these factors affect our 

ability to estimate the probability that the dose associated with a given locale is representative of 

that which produced the ensuing cancerous lesion. 

For example, the percentage of total lymphocytes represented by B lymphocytes within the 

various body compartments ranges from ≤1% in skin, thymus, and the epithelium of the lung or 

digestive tract, to ≥50% in the spleen, tonsils, red bone marrow, and greater omentum. There is 

also a wide variation in the distributions of specific types of lymphocytes within different 

microenvironments of lymphoid organs or tissues (Westermann and Pabst 1992). There are 5 or 

more subsets of B cells, including several different types of memory cells, in human blood 

(Klein et al. 1998; van Zelm et al. 2007). Likewise, there are 5−7 different subtypes of B cells, 

including several different types of memory cells, in human tonsils (Liu et al. 1997; van Zelm et 

al. 2007). About 50−90% of the intraepithelial lymphocytes in the palatine tonsils are B cells, 

while the majority of the B cells in the crypt epithelium are mature memory B cells (Nave et al. 

2001). 

The extent to which lymphocytes are localized within the lymphatic system, and even within 

a given lymph node, thus depends on the type of lymphocyte (e.g., B- or T-cell) and its specific 

origin (see footnotes and associated text in Appendix B) and its experience with antigen, 

including involvement of the germinal center reaction. Some memory B-cells tend to remain in 

the lymph nodes while activated T-cells usually leave the lymph nodes via the efferent lymph 

and circulate more freely. However, T-cell-dependent B-cells that become memory cells via the 

germinal center reaction often migrate to sites other than the lymph nodes, including the bone 

marrow and antigen-draining sites in mucosal associated lymphatic tissue, Peyer’s patches, the 

subepithelial region of the tonsils, and splenic marginal zones, where they may persist for a 

considerable period of time (months or more) (Liu and Arpin 1997; Paramithiotis and Cooper 

1997; Hay and Andrade 1998; Angelin-Duclos et al. 2000; Chiorazzi 2007). 

Thus, how often lymphocytes might be exposed to plutonium α particles in the thoracic 

lymph nodes, for example, could vary significantly depending on the type of lymphocyte 
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precursor, the level of “antigen experience,” and whether it was activated with T-cell help in the 

germinal center.10 

In addition, the doses from plutonium α particles to each of the lymphocyte precursors for 

CLL in the groups identified above would depend on their effective lifespan. Estimated mean 

lifespan for lymphocytes is about 4−6 months, but individual lifetimes can range from a few days 

to several months for some B cells and from several years to a human lifetime for memory cells 

(Chiorazzi 2007; Rai 2007c). Marginal zone B cells are predominantly memory cells, and most 

are long-lived, with some expected to survive as long as their host (Boyd 2007). The lifespan is 

again determined by the type of lymphocyte involved, its temporal pattern of exposure to 

antigen, and nature of involvement of the germinal center reaction (Angelin-Duclos et al. 2000; 

Chiorazzi 2007). 

Uncertainty about the influence of each of these factors on a given lymphocyte and about the 

lymphocyte precursor for CLL makes it difficult to choose which estimate of lifespan would be 

most appropriate to apply in a dose reconstruction effort. For example, active suppression of 

terminal differentiation gives the memory lymphocyte a self-renewing capability like that of 

stem cells in other organ systems (Fearon et al. 2001) and thus makes it a seemingly good 

candidate for a CLL precursor. Under these conditions, an assumption of a lymphocyte lifespan 

as long as a human lifetime might be a technically defensible choice. Unfortunately, there is not 

yet a consensus on whether the memory cell is the most likely precursor for CLL (Keating et al. 

2003; Stevenson and Caligaris-Cappio 2004; Chiorazzi et al. 2005; Hervé et al. 2005; CaligarisCappio and Ghia 2007; Chiorazzi 2007; Ghia et al. 2007; Zent 2007). Even if agreement could 

be reached on this point, uncertainties of the types described in the previous paragraph limit the 

estimation of a meaningful dose for use in IREP with the proposed CLL model. 

Finally, only those lymphatic cells or CLL precursors (see footnote 10) located within the 

thoracic lymph nodes would be exposed at a high dose rate over the entire lifetime of an exposed 

person. Although the structural cells of the lymph nodes themselves are not expected to be 

precursors for CLL, radiogenic changes in these cells, including induction of senescence, 11 could 

 

10 A possible but seemingly low probability scenario could involve a mature B-lymphocyte (e.g., a memory cell) that 

localized in one of the thoracic lymph nodes for an entire human lifetime. Unfortunately, it is not currently possible 

to estimate the probability of such a scenario based on current limited information. 

11 It is more likely that such changes would result from buildup of senescent cells as part of the aging process rather 

than as an effect of radiation exposure. This could explain in part why CLL is primarily a disease of old age. 
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potentially affect the tissue microenvironment in a manner that would promote development of 

CLL, e.g., by rescuing CLL cells from apoptosis or supporting the expansion of CLL clones 

(Table 1; Stevenson and Caligaris-Cappio 2004; Chiorazzi et al. 2005).12, 13 

However, even under such assumptions, the dose rate to this restricted part of the lymphatic 

system (i.e., the thoracic lymph nodes) is not necessarily meaningful for estimating the risk of 

developing CLL (or other types of NHL, for that matter). The reason is that a substantial body of 

research indicates that the mass of the whole organ or tissue is the appropriate reference to be 

considered in calculating radiation dose and estimating radiation risk. The problem in the case of 

CLL is to ascertain just what constitutes the “whole organ or tissue” (see footnote 2). 

The frequency of chromosome aberrations and induction of liver cancer was not affected by 

spatial inhomogeneity in the dose delivered to Chinese hamster liver cells by plutonium α

particles (Brooks et al. 1974, 1983). Based on a wide range of information, including results 

from studies of the effects of variations in spatial distribution of doses from plutonium deposited 

in the lungs of dogs, hamsters, and rats (Bair et al. 1974), the NCRP (1975) concluded that 

“particulate plutonium in the lung is no greater hazard than the same amount of plutonium more 

uniformly distributed throughout the lung.” Brooks (2003) has recently concluded that bystander 

effects make the organ or tissue respond as a whole and thus indicate that radiation effects may 

be related to organized responses at the level of the organ or tissue rather than to alterations 

induced in single cells. Thus, it is not clear that the dose within an individual component of the 

lymphatic system, as opposed to an estimate of the dose to the entire “system”—assuming a 

meaningful definition of the term is possible for CLL—would necessarily be a meaningful 

quantity with respect to induction of CLL (or other types of NHL). 

 

12 All experts contacted by NIOSH/OCAS (Chiorazzi 2007; Kay 2007; Rai 2007; Zent 2007) agreed that CLL cells 

are sustained and stimulated to proliferate by external signals from other cell types (stromal cells in bone marrow, T 

cells in lymph nodes, and nurse-like cells in peripheral blood—or perhaps elsewhere; see footnote 13) or soluble 

factors (cytokines, chemokines, and/or antigens). CLL B cells seem to need close or intimate contact with the cell 

types just mentioned and are drawn to them by chemokines. Although there is extensive current research in this area, 

including that by one of the experts (Kay), it is not possible to assess which signals are most important or how many 

are needed to sustain a CLL clone. Chiorazzi and Kay seem to agree that a multiplicity of such signals is probably 

needed. Kay also pointed out that it is not currently known whether genetic or cytogenetic alterations in these other 

cell types play a role in the etiology of CLL. 

13 Nurse-like cells were first recognized in situ in the thymus, where they form characteristic complexes with 

immature T cells and play an important role in T-cell maturation and differentiation. Mature counterparts of bloodderived nurse-like cells are likely to play a role in protecting CLL B cells from apoptosis in distinct lymphoid (and 

non-lymphoid) microenvironments rather than in the bloodstream (Burger et al. 2000). 
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It currently appears that reconstructing internal doses or external non-uniform doses for 

claimants with CLL (and related cancers) represents a major technical challenge given current 

uncertainties of the types just discussed. Effective implementation of the CLL model in cases in 

which claimants received non-uniform internal or external exposures hinges on whether 

meaningful estimates of dose can be reconstructed and appears to require a significant research 

effort, outside the scope of this report. An analysis of several approaches to determine a 

meaningful dose from non-uniform exposures for the purpose of assessing the risk of CLL 

(Apostoaei and Trabalka 2009) has recently been completed. 

7. CONCLUSIONS

Based on the most recent information on CLL incidence reported in epidemiological studies 

and the latest estimates of ERR/Sv for NHL mortality in male atomic-bomb survivors reported 

by Richardson et al. (2009), it is concluded that the IREP lymphoma and multiple myeloma 

model should be used for CLL, but only as an interim solution because of the concerns 

enumerated in Section 5.2. Whereas neither the earlier analysis of mortality from malignant 

lymphoma in the LSS cohort by Pierce et al. (1996), based on follow-up through 1990, nor the 

UNSCEAR analysis of LSS data on CLL incidence, based on follow-up through 1987, showed a 

significant dose-response in males or females, the updated analysis by Richardson et al. (2009), 

which was based on 10 years of additional follow-up, now shows a highly significant response 

for mortality from NHL in males. Thus, we think that the DS02 data for lymphoma incidence, 

which will have an even longer follow-up when published, could show significantly higher risks 

for both males and females. 

Therefore, a high priority will be assigned to development of a new NHL model to cover 

CLL, and other diseases now included in the revised definition of NHL, after the RERF-NCI 

team publishes the new LSS data. Reanalysis of the Japanese A-bomb survivor data for NHL (or, 

barring that, for the lymphoma and multiple myeloma grouping), using an appropriate surrogate 

for whole-body dose, and with a focus on deriving age-at-exposure and attained-age 

dependencies that are independent of those derived for solid tumors, could potentially result in 

significant changes to the risk coefficients in the proposed CLL model. 
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8. REFERENCES

Abramenko, I., Bilous, N., Chumak, A., Davidova, E., Kryachok, I., Martina, Z., Nechaev, S., 

Dyagil, I., Bazyka, D. and Bebeshko, V. 2008. Chronic lymphocytic leukemia patients 

exposed to ionizing radiation due to the Chernobyl NPP accident—with focus on 

immunoglobulin heavy chain analysis. Leuk. Res. 32: 535−545. 

Anderson, A. O. 2002. Lymphocyte Recirculation and Homing in Immunity. 

(http://www.geocities.com/CapeCanaveral/Hangar/1962/page2.html), January 1, 2002. 

Andrade, W. N., Johnston, M. G., and Hay, J. B. 1998. The relationship of blood lymphocytes to 

the recirculating lymphocyte pool. Blood 91:1653−1661. 

Angelin-Duclos, C., Cattoretti, G., Lin, K.-I., and Calame, K. 2000. Commitment of B 

lymphocytes to a plasma cell fate is associated with Blimp-1 expression in vivo. J. Immunol.

165: 5462−5471. 

Apostoaei A.I. and Trabalka J.R. 2009. Review, synthesis, and application of information on 

the human lymphatic system to radiation dosimetry for chronic lymphocytic leukemia. 

Draft report; SENES Oak Ridge, Inc., Oak Ridge, TN. 

Bair, W. J., Richmond, C. R., and Wachholz, B. W. 1974. A Radiobiological Assessment of the 

Spatial Distribution of Radiation dose from Inhaled Plutonium. U.S. Atomic Energy 

Commission (AEC) Report WASH-1320, September 1974. AEC, Washington, DC. 

Bakkus, M. H., Van Riet, I., Van Camp, B., and Thielemans, K. 1994. Evidence that the 

clonogenic cell in multiple myeloma originates from a pre-switched but somatically mutated 

cell. Br. J. Haematol. 87: 68−74. 

Berkow, R. 1992. Chronic lymphocytic leukemia (CLL), pp. 1241−1243 in The Merck Manual 

of Diagnosis and Therapy, 16th edition, R. Berkow (editor-in chief). Merck Research 

Laboratories, Rahway, NJ. 

Boice, J. D., Jr. 2002. Radiation-induced leukemia, pp. 152−169 in Leukemia, 7th edition, E. S. 

Henderson, T. A. Lister, and M. F. Greaves (editors). Saunders, Philadelphia, PA. 

Boice, J. D., Jr. 2005. Reconsideration of chronic lymphocytic leukemia for purposes of 

compensation. Report submitted to NIOSH/OCAS, January 7, 2005. 

Boice, J. D., Jr., Day, N. E., Andersen, A., Brinton, L. A., Brown, R., Choi, N. W., Clarke, E. A., 

Coleman, M. P., Curtis, R. E., Flannery, J. T., Hakama, M., Hakulinen, T., Howe, G. R., 

Jensen, O. M., Kleinerman, R. A., Magnin, D., Magnus, K., Makela, K., Malker, B., Miller, 

A. B., Nelson, N., Patterson, C. C., Pettersson, F., Pompe-Kirn, V., Primic-Zakelj, M., Prior, 

P., Ravnihar, B., Skeet, R. G., Skjerven, J. E., Smith, P. G., Sok, M., Spengler, R. F., Storm, 

H. H., Stovall, M., Tomkins, G. W. O., and Wall, C. 1985. Second cancers following 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 37 

radiation treatment for cervical cancer. An international collaboration among cancer 

registries. J. Natl Cancer I. 74:955−975. 

Boice, J. D., Jr., Blettner, M., Kleinerman, R. A., Stovall, M., Moloney, W. C., Engholm, G., 

Austin, D.F., Bosch, A., Cookfair, D. L., Krementz, E. T., Latourette, H. B., Peters, L. J., 

Schulz, M. D., Lundell, M., Pettersson, F., Storm, H. H., Bell, C. M. J., Coleman, M. P., 

Fraser, P., Palmer, M., Prior, P., Choi, N. W., Hislop, T. G., Koch, M., Robb, D., Robson, 

D., Spengler, R. F., von Fournier, D., Frischkorn, R., Lochmüller, H., Pompe-Kirn, V., 

Rimpela, A., Kjørstad, K., Pejovic, M. H., Sigurdsson, K., Pisani, P., Kucera, H., and 

Hutchison, G. B. 1987. Radiation dose and leukemia risk in patients treated for cancer of the 

cervix. J. Natl Cancer I. 79:1295−1311. 

Blum, K. S., and Pabst, R. 2007. Lymphocyte numbers and subsets in the human blood: Do they 

mirror the situation in all organs? Immunol. Lett. 108:45−51. 

Bowers, W., and Hunt, R. 2007. Immunology – Chapter Nine: Cells Involved in Immune 

Responses. Microbiology and Immunology On-Line, 

(http://pathmicro.med.sc.edu/bowers/immune%20cells.htm), last modified: April 5, 2007. 

University of South Carolina, Columbia, SC. 

Boyd, D. 2007. Marginal Zone B Cell (Monocytoid B cell, MALT B cell), 

(http://www.healthsystem.virginia.edu/hematology/HessEDD/immune%20cells.htm), last 

modified: June 19, 2007. Division of Hematology/Oncology, University of Virginia, 

Charlottesville, VA. 

Braig, M., Lee, S., Loddenkemper, C., Rudolph, C., Peters, A. H. F. M., Schlegelberger, B., 

Stein, H., Dörken, B., Jenuwein, T., and Scmitt, C. A. 2005. Oncogene-induced senescence 

as an initial barrier in lymphoma development. Nature 436:660−665. 

Brandtzaeg, P., and Johansen, F.-E. 2005. Mucosal B cells: phenotypic characteristics, 

transcriptional regulation, and homing properties. Immunol. Rev. 206:32−63. 

Brandtzaeg, P., Kiyono, H., Pabst, R., and Russell, M. W. 2008. Terminology: nomenclature of 

mucosa-associated lymphoid tissue. Mucosal Immunol. 1:31−37. 

Brooks, A. L. 2003. Developing a scientific basis for radiation risk estimates: Goal of the DOE 

Low Dose Research Program. Health Phys. 85:85–93. 

Brooks, A. L., Retherford, J. C., and McClellan, R. O. 1974. Effect of 239PuO2 particle number 

and size on the frequency and distribution of chromosome aberrations in the liver of the 

Chinese hamster. Radiat. Res. 59:693−709. 

Brooks, A. L., Benjamin, S. A., Hahn, F. F., Brownstein, D. G., Griffith, M. W., and McClellan, 

R. O. 1983. The induction of liver tumors by 239PuO2 citrate or 239PuO2 particles in the 

Chinese hamster. Radiat. Res. 96:135−151. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 38 

Brown, A. R. 1992. Immunological functions of splenic B-lymphocytes. Crit. Rev. Immunol. 

11:395−417. 

Burger, J. A., Tsukada, N., Burger, M., Zvaifler, N. J., Dell’Aquila, M., and Kipps, T. J. 2000. 

Blood-derived nurse-like cells protect chronic lymphocytic leukemia B cells from 

spontaneous apoptosis through stromal cell−derived factor-1. Blood 96:2655−2663. 

Caligaris-Cappio, F. 2000. Biology of chronic lymphocytic leukemia. Rev. Clin. Exp. Hematol. 

4:5−21. 

Caligaris-Cappio, F., and Ghia, P. 2007. The normal counterpart to the chronic lymphocytic 

leukemia B cell. Best Pract. Res. Clin. Haematol. 20:385−397. 

Cardis, E., Vrijheid, M., Blettner, M., Gilbert, E., Hakama, M., Hill, C., Howe, G., Kaldor, J., 

Muirhead, C. R., Schubauer-Berigan, M., Yoshimura, T., Bermann, F., Cowper, G., Fix, J., 

Hacker, C., Heinmiller, B., Marshall, M., Thierry-Chef, I., Utteback, D., Ahn, Y.-O., 

Amoros, E., Ashmore, P., Auvinen, A., Bae, J.-M., Bernar, J., Biau, A., Combalot, E., 

Deboodt, P., Diez Sacristan, A., Eklöf, M., Engels, H., Engholm, G., Gulis, G., Habib, R. R., 

Holan, K., Hyvonen, H., Kerekes, A., Kurtinaitis, J., Malker, H., Martuzzi, M., Mastaukas, 

A., Monnet, A., Moser, M., Pearce, M. S., Richardson, D. B., Rodriguez-Artalejo, F., Rogel, 

A., Tardy, H., Telle-Lamberton, M., Turai, I., Usel, M., and Veress, K. 2007. The 15-

country collaborative study of cancer risk among radiation workers in the nuclear industry: 

estimates of radiation-related cancer risks. Radiat. Res. 167:396−416. 

Chiorazzi, N. 2007. Comments on questions about chronic lymphocytic leukemia. Report 

submitted to NIOSH/OCAS, August 18, 2007. 

Chiorazzi, N., Rai, K. R., and Ferrarini, M. 2005. Mechanisms of disease: Chronic lymphocytic 

leukemia. N. Engl. J. Med. 352:804−815. 

Crespo, M., Bosch, F., Villamor, N., Bellosillo, B., Colomer, D., Rozman, M., Marcé, S., LópezGuillermo, A., Campo, E., and Montserrat, E. 2003. ZAP-70 expression as a surrogate for 

immunoglobulin-variable-region mutations in chronic lymphocytic leukemia. N. Engl. J. 

Med. 348:1764−1775. 

Crowther, M. A. 2004. Untitled report submitted to NIOSH/OCAS, November 2004. 

Dewald, G. W., Brockman, S. R., Paternoster, S. F., Bone, Nancy, D., O’Fallon, J. R., Allmer, 

C., James, C. D., Jelinek, D. F., Tschumper, R. C., Hanson, C. A., Pruthi, R. K., Witzig, T. 

E., Call, T. G., and Kay, N. E. 2003. Chromosome anomalies detected by interphase in situ

hybridization: correlation with significant biological features of B-cell chronic lymphocytic 

leukemia. Br. J. Haematol. 121:287−295. 

Dores, G. M., Anderson, W. F., Curtis, R. E., Landgren, O., Ostroumova, E., Bluhm, E. C., 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 39 

Rabkin, C. S., Devesa, S. S., and Linet, M. S. 2007. Chronic lymphocytic leukemia and 

small lymphocytic leukemia: overview of the descriptive epidemiology. Brit. J. Haematol.

139:809−819.

El Ghissassi, F., Baan, R., Straif, K., Grosse, Y., Secretan, B., Bouvard, V., Benbrahim-Tallaa, 

Guha, N., Freeman, C., Galichet, L., and Cogliano, V. 2009. A review of human 

carcinogens—Part D: radiation. Lancet Oncol. 28:751−752. 

Fearon, D. T., Manders, P., and Wagner, S. D. 2001. Arrested differentiation, the self-renewing 

memory lymphocyte, and vaccination. Science 293: 248−250.

Ganusov, V. V., and De Boer, R. J. 2007. Do most lymphocytes in humans really reside in the 

gut? Trends Immunol. 28:514−518. 

George, E. D., and Sadovsky, R. 1999. Multiple myeloma: Recognition and management. 

American Family Physician (http://www.aafp.org/afp/990401ap/1885.html), April 1, 1999.

Ghia, P., Scielzo, C., Frenquelli, M., Muzio, M., and Caligaris-Cappio, F. 2007. From normal to 

clonal B cells: Chronic lymphocytic leukemia (CLL) at the crossroad between neoplasia and 

autoimmunity. Autoimmun. Rev. 7:127−131. 

Grund, S. 2004. Medical Encyclopedia: Hodgkin’s lymphoma. Medline Plus 

(http://www.nlm.nih.gov/medlineplus/print/ency/article/000580.htm), May 3, 2004. 

Hall, P., Boice, J. D., Jr., Berg, G., Bjelkengren, G., Ericsson, U.-B., Hallquist, A., Lidberg, M., 

Lundell, G., Mattsson, A., Tennvall, J., Wiklund, K., and Holm, L.-E. 1992. Leukaemia 

incidence after iodine-131 exposure. Lancet 340:1−4. 

Hamblin, T. J. 2008. Have we been wrong about ionizing radiation and chronic lymphocytic 

leukemia. Leuk. Res. 32:523−525. 

Harris, N. L., Jaffe, E. S., Diebold, J., Flandrin, G., Muller-Hermelink, H. K., Vardiman, J., 

Lister, T. A., and Bloomfield, C. D. 1999. World Health Organization classification of 

neoplastic diseases of the hematopoietic and lymphoid tissues: Report of the clinical 

advisory committee meeting─Airlie House, Virginia, November 1997. J. Clin. Oncol. 17: 

3835−3849. 

 

Hay, J. B., and Andrade, W. N. 1998. Lymphocyte recirculation, exercise, and immune 

responses. Can. J. Physiol. Pharmacol. 76:490−496. 

Hervé, M., Xu, K., Ng, Y.-S., Wardemann, H., Albesiano, E., Messmer, B. T., Chiorazzi, N., and 

Meffre, E. 2005. Unmutated and mutated chronic lymphocytic leukemias derive from selfreactive B cell precursors despite expressing different antibody reactivity. J. Clin. Invest.

115:1636−1643. 

 (IARC) International Agency for Research on Cancer. 2000. Ionizing Radiation, Part I: X- and 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 40 

gamma (γ) –radiation and neutrons. IARC Monographs on the Evaluation of Carcinogenic 

Risk to Humans, Volume 75. IARC, Lyon, France. 

(ICRP) International Commission on Radiological Protection. 1994. Human Respiratory Tract 

Model for Radiological Protection, ICRP Publication 60. Ann. ICRP 24(1–3). Pergamon 

Press, Oxford. 

(ICRP) International Commission on Radiological Protection. 2002. Basic Anatomical and 

Physiological Data for Use in Radiological Protection: Reference Values, ICRP Publication 

89. Ann. ICRP 32(3–4). Pergamon Press, Oxford. 

Inskip, P. D., Kleinerman, R. A., Stovall, M., Cookfair, D. L., Hadjimichael, O., Moloney, W. 

C., Monson, R. C., Thompson, W. T., Wactawski-Wende, J., Wagoner, J. K., and Boice, J. 

D., Jr. 1993. Radiat. Res. 135:108−124. 

Kay, N. E. 2007. Review: Evidence for the interaction of CLL leukemic B cells with stromal 

tissue and its consequences for the leukemic B cell. Report submitted to NIOSH/OCAS, July 

17, 2007. 

Keating, M. J., Chiorazzi, N., Messmer, B., Damle, R. N., Allen, S. L., Rai, K. R., Ferrrarini, M., 

and Kipps, T. J. 2003. Biology and treatment of chronic lymphocytic leukemia. Hematology 

2003:154−175. 

Kesminiene, A., Evrard, A.-S., Ivanov, V. K., Malakhova, I., Kurtinaitis, J., Stengrevics, A., 

Tekkel, M., Anspaugh, L. R., Bouville, A., Chekin, S., Chumak, V. V., Drozdovitch, D., 

Gapanovitch, V., Golovanov, I., Hubert, P., Ilichev, S. V., Khait, S. E., Kryuchkov, V. P., 

Maceika, E., Maksyoutov, M., Mirkhaidorov, A. K., Polyakov, S., Shchukina, N., Tenet, V., 

Tserakhovitch, T. I., Tsykalo, A., Tukov, A. R., and Cardis, E. 2008. Risk of hematological 

malignancies among Chernobyl liquidators. Radiat. Res. 170:721−735. 

Klein, U., Goosens, T., Fischer, M., Kanzler, H., Braeuniger, A., Rajewsky, K., and Küppers, R. 

1998. Somatic hypermutation in normal and transformed human cells. Immunol. Rev.

162:261−280. 

Klein, U., Tu, Y., Stolovitsky, G. A., Mattioli, M., Cattoretti, G., Husson, H., Freedman, A., 

Inghirami, G., Cro, L., Baldini, L., Neri, A., Califano, A., and Dalla-Favera, R. 2001. Gene 

expression profiling of B cell chronic lymphocytic leukemia reveals a hommogeneous 

phenotype related to memory cells. J. Exp. Med. 194: 1625−1638. 

Kraal, G. 1992. Cells in the marginal zone of the spleen. Int. Rev. Cytol. 132:31−74. 

Kryuchkov, V., Chumak, V., Maceika, E., Anspaugh, L. R., Cardis, E., Bakhanova, E., 

Golovanov, I., Drozdovitch, V., Luckyanov, N., Kesminiene, A., Voilleque, P., and 

Bouville, A. 2009. RADRUE method for reconstruction of external photon doses for 

Chernobyl liquidators in epidemiological studies. Health Phys. 97, 275–298. 

Kyasa, M. J., Hazlett, L., Parrish, R. S., Schichman, S. A., and Zent, C. S. 2004. Veterans with 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 41 

chronic lymphocytic leukemia/small lymphocytic leukemia (CLL/SLL) have a markedly 

increased rate of second malignancy, which is the most common cause of death. Leuk. 

Lymphoma 45: 507−513. 

Land, C. E. 2000. Unpublished data evaluated during development of IREP. 

Land, C. E., Gilbert, E. S., Smith, J. M., Hoffman, F. O., Apostoaei, I., Thomas, B., and Kocher, 

D. C. 2003. Report of the NCI-CDC Working Group to Revise the 1985 NIH 

Radioepidemiological Tables. NIH Publication No. 03-5387. U.S. Department of Health and 

Human Services, National Institutes of Health, National Cancer Institute, Bethesda, MD. 

Linet, M. S., Schubauer-Berigan, M. K., Weisenburger, D. D., Richardson, D. B., Landgren, O., 

Blair, A., Silver, S. R., Field, R. W., Caldwell, G., Hatch, M., and Dores, G. M. 2007. 

Chronic lymphocytic leukemia: an overview of aetiology in light of recent developments in 

classification and pathogenesis. Brit. J. Haematol. 139:672−686.

Linet, M. S., Freedman, D. M., Mohan, A. K., Doody, M. M., Ron, E., Mabuchi, K., Alexander, 

B. H., Sigurdson, A., and Hauptman, M. 2005. Incidence of hematopoietic malignancies in 

U.S. radiologic technologists. Occup. Environ. Med. 62:861−867. 

Liu, Y.-J., and Arpin, C. 1997. Germinal center development. Immunol. Rev. 156:111−126. 

Lonial, S. 2005. Introduction to Myeloma, Multiple Myeloma Research Foundation 

(http://www.multiplemyeloma.org/about_myeloma/), September 9, 2005. 

Magrath, I. 1992. Molecular basis of lymphomagenesis. Cancer Res. (Suppl.) 52:5529s−5540s. 

Marti, G., Abbasi, F., Raveche, E., Rawstron, A. C., Ghia, P., Aurran, T., Caporaso, N., Shim, Y. 

K., and Vogt, R.F. 2007. Overview of monoclonal B-cell lymphocytosis. Brit. J. Haematol.

139:701−708. 

McHeyzer-Williams, L. J., and McHeyzer-Williams, M. G. 2005. Antigen-specific memory cell 

development. Annu. Rev. Immunol. 23:487−513. 

Meffre, E., Schaefer, A., Wardemann, H., Wilson, P., Davis, E., and Nussenzweig, M. C. 2004. 

Surrogate light chain expressing human peripheral B cells produce self-reactive antibodies. 

J. Exp. Med. 199:145−150. 

Mellors, R. C. 2002. Immunopathology. 

(http://edcenter.med.cornell.edu/CUMC/PathNotes/Immunopathology/Immuno_01.html), 

last modified: August 27, 2002. Cornell University Medical Center, Ithaca, NY. 

Montoya, M. C., Holtmann, K., Snapp, K. R., Borges, E., Sánchez-Madrid, F., Luscinsckas, F. 

W., Kansas, G., Vestweber, D., and de Landázuri, M. O. 1999. Memory B lymphocytes 

from secondary lymphoid organs interact with E-selectin through a novel glycoprotein 

ligand. J. Clin. Invest. 103:1317−1327. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 42 

Muirhead, C. R., O’Hagan, J. A., Haylock, R. G. E., Phillipson, M. A., Willcock, T., Berridge, 

G. L. C., and Zhang, W. 2009. Mortality and cancer incidence following occupational 

radiation exposure: third analysis of the National Registry for Radiation Workers. Brit. J. 

Cancer 100:206–212. 

Nave, H., Gebert, A., and Pabst, R. 2001. Morphology and immunology of the human palatine 

tonsil. Anat. Embryol. 204:367−373. 

NCI (National Cancer Institute). 2009a. Adult Non-Hodgkin Lymphoma (PDQ®): Treatment, 

Health Professional Version, http://www.cancer.gov/cancertopics/pdq/treatment/adult-nonhodgkins/healthprofessional/allpages/print, last modified: August 28, 2009. NCI, U.S. 

National Institutes of Health, Bethesda, MD. 

NCI (National Cancer Institute). 2009b. Chronic Lymphocytic Leukemia (PDQ®): Treatment, 

Health Professional Version, 

http://www.cancer.gov/cancertopics/pdq/treatment/CLL/healthprofessional/allpages/print, 

last modified: May 22, 2009. NCI, U.S. National Institutes of Health, Bethesda, MD. 

NCI (National Cancer Institute). 2009c. Adult Hodgkin Lymphoma (PDQ®): Treatment, 

Health Professional Version, 

http://www.cancer.gov/cancertopics/pdq/treatment/adulthodgkins/healthprofessional/allpage

s/print, last modified: May 15, 2009. NCI, U.S. National Institutes of Health, Bethesda, MD. 

NCRP (National Council on Radiation Protection and Measurements). 1975. Alpha-emitting 

Particles in Lungs. NCRP Report 46. NCRP, Washington, DC. 

NIOSH (National Institute for Occupational Safety and Health). 2004. Chronic Lymphocytic 

Leukemia: Annotated Bibliography, July 21, 2004 draft. NIOSH, Health-Related Energy 

Research Branch, Cincinnati, OH. 

NRC (National Research Council), Committee to Assess Health Risks from Exposure to Low 

Levels of Ionizing Radiation. 2006. Health Risks from Exposure to Low Levels of Ionizing 

Radiation, BEIR VII−Phase 2. The National Academies Press, Washington, DC. 

ORAU (Oak Ridge Associated Universities). 2006. Selection for Internal and External 

Dosimetry Target Organs for Lymphatic/Hematopoietic Cancers, Office of Compensation 

Analysis and Support Technical Information Bulletin 12, Rev. 1. ORAU, Oak Ridge, TN.

Pabst, R. 1988. The spleen in lymphocyte migration. Immunol. Today 9:43−45. 

Paramithiotis, E., and Cooper, M. D. 1997. Memory B lymphocytes migrate to bone marrow in 

humans. Proc. Natl. Acad. Sci. USA 94:208−212. 

Parkin D.M., Whelan S.L., Ferlay J., Raymond L., Young J (eds.). 1997. Cancer in five 

continents, Volume VII. IARC Scientific Publication No. 143, Lyon, France. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 43 

Pillai, S., Cariappa, A., and Moran, S. T. 2005. Marginal zone B cells. Annu. Rev. Immunol. 23: 

161−196. 

Preston, D. L., Kusumi, S., Tomonaga, M., Izumi, S., Ron, E., Kuramoto, A., Kamada, N., Dohy, 

H., Matsui, 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. Radiat. Res. 137 (Suppl.), S68–S97. 

Preston, D. L., Lubin, J. H., Pierce, D. A., and McConney, M. E. 1998. EPICURE Version 2.10. 

Hirosoft International Corporation, Seattle, WA. 

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. Radiat. Res. 162:377–389. 

Rai, K. R. 2007a. Re: NIOSH inquiry. Report submitted to NIOSH/OCAS, May 8, 2007. 

Rai, K. R. 2007b. Pathophysiologic mechanisms of chronic lymphocytic leukemia and their 

application to therapy. Exp. Haematol. 35:134−136. 

Rai, K. R. 2007c. Re: Your review on CLL for NIOSH. Report submitted to NIOSH/OCAS, 

June 8, 2007. 

Rawstron, A. C., Green, M. J., Kuzmicki, A., Kennedy, B., Fenton, J. A. L., Evans, P. A. S., 

O’Connor, S. J. M., Richards, S. J., Morgan, G. J., Jack, A. S., and Peter Hillmen. 2002. 

Monoclonal B lymphocytes with the characteristics of “indolent” chronic lymphocytic 

leukemia are present in 3.5% of adults with normal blood counts: revelations from the B-cell 

receptor. Blood 100:635−639. 

Rawstron, A. C., Bennett, F., and Hillmen, P. 2007. The biological and clinical relationship 

between CD5+23+ monoclonal B-cell lymphocytosis and chronic lymphocytic leukemia. 

Brit. J. Haematol. 139:701−708. 

Rericha, V., Kulich, M., Reicha, R., Shore, D. L., and Sandler, D. P. 2006. Incidence of 

leukemia, lymphoma, and multiple myeloma in Czech uranium miners: a case-cohort study. 

Environ. Health Persp. 114:818−822. 

Richardson, D. B. 2004. Chronic Lymphocytic Leukemia: Reconsideration of Exclusion from 

Eligibility for Compensation Under EEOICPA. Report submitted to NIOSH/OCAS, 

November 2004. 

Richardson, D. B., Wing, S., Schroeder, J., Schmitz-Feuerhake, I., and Hoffmann, W. 2005. 

Ionizing radiation and chronic lymphocytic leukemia. Environ. Health Persp. 113:1−5. 

Richardson, D. B., Sugiyama, H., Wing, S., Sakata, R., Grant, E., Shimizu, Y., Nishi, N., Geyer, 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 44 

S., Soda, M., Suyama, A., Kasagi, F., and Kodama, K. 2009. Positive associations between 

ionizing radiation and lymphoma mortality among men. Am. J. Epidemiol. 169:969−976. 

Romanenko, A. Ye., Finch, S. C., Hatch, M., Lubin, J. H., Bebeshko, V. G., Bazyka, D. A., 

Gudzenko, N., Dyagil, I. S., Reiss, R. F., Bouville, A., Chumak, V. V., Trotsiuk, N. K., 

Babkina, N. G., Belyayev, Y., Masnyk, I., Ron, E., Howe, G. R., and Zablotska, L. B. 2008. 

The Ukrainian-American study of leukemia and related disorders among Chornobyl cleanup 

workers from the Ukraine: III. Radiation risks. Radiat. Res. 170:711−720. 

Rosenwald, A., Alizadeh, A. A., Widhopf, G., Simon, R., Davis, R. E., Yu, X., Yang, L., 

Pickeral, O. K., Rassenti, L. Z., Powell, J. Botstein, D., Byrd, J. C., Grever, M. R., Cheson, 

B. D., Chiorazzi, N., Wilson, W. H., Kipps, T. J., Brown, P. O., and Staudt, L. M. 2001. 

Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell 

chronic lymphocytic leukemia. J. Exp. Med. 194:1639−1647. 

Schubauer-Berigan, M. K., Daniels, R. D., Fleming, D. A., Markey, A. M., Couch, J. R., 

Ahrenholz, S. H., Burphy, J. S., Anderson, J. L., and Tseng, C.-Y. 2007. Chronic 

lymphocytic leukemia and radiation: findings among workers at five U. S. nuclear facilities 

and a review of the recent literature. Brit. J. Haematol. 139:799−808.

 (SEER) Surveillance, Epidemiology, and End Results Program. 2005. (www.seer.cancer.gov) 

SEER*Stat Database: Incidence - SEER 13 Regs Public-Use, Nov 2004 Sub for Expanded 

Races (1992-2002), National Cancer Institute, DCCPS, Surveillance Research Program, 

Cancer Statistics Branch, released April 2005, based on the November 2004 submission. 

Silver, S. R., Hiratzka, S. L., Schubauer-Berigan, M. K., and Daniels, R. D. 2007. Chronic 

lymphocytic leukemia radiogenicity: a systematic review. Cancer Causes Control 18: 

1077−1093. 

Stevenson, F. K., and Caligaris-Cappio, F. 2004. Chronic lymphocytic leukemia: revelations 

from the B-cell receptor. Blood 103:4389−4395. 

Stevenson, F., Sahota, S., Zhu, D., Ottensmeier, C., Chapman, C., Oscier, D., and Hamblin, T. 

1998. Insight into the origin and clonal history of B-cell tumors as revealed by analysis of 

immunoglobulin variable region genes. Immunol. Rev. 162:247–259. 

Stilgenbauer, S., Bullinger, L., Lichter, P., Döhner, H., and the German CLL Study Group. 2002. 

Genetics of chronic lymphocytic leukemia: genomic aberrations and Vh gene mutation status 

in pathogenesis and clinical course. Leukemia 16:993−1007. 

Teoh, G., and Anderson, K. C. 1997. Interaction of tumor and host cells with adhesion and 

extracellular matrix molecules in the development of multiple myeloma. Haematol. Oncol. 

Clin. North Am. 11:27−42. 

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 occurrence in atomic 

bomb survivors. Part II: Solid tumors, 1958–1987. Radiat. Res. 137, S17–S67. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 45 

 

Tierens, A., Delabie, J., Michiels, L., Vandenberghe, P. and De Wolf-Peeters. 2000. Marginalzone B cells in the human lymph node and spleen show somatic hypermutation and display 

clonal expansion. Blood 96:1612−1614. 

Trepel, F. 1974. Number and distribution of lymphocytes in man. A critical analysis. Klin. 

Wschr. 52:511−515. 

(UNSCEAR) United Nations Scientific Committee on the Effects of Atomic Radiation. 2008. 

Effects of Ionizing Radiation, Volume I, Annex A, Epidemiological Studies of Radiation and 

Cancer. United Nations, New York. 

Van Zelm, M. C., Szczepanski, T., van der Burg, M., and van Dongen, J. J. M. 2007. Replication 

history of B lymphocytes reveals homeostatic proliferation and extensive antigen-induced 

cell expansion. J. Exp. Med. 204:645−655. 

Voutsadakis, I. A. 2000. Apoptosis and the pathogenesis of lymphoma. Acta Oncol. 39: 

151−156. 

Vrijheid, M., Cardis, E., Ashmore, P., Auvinen, A., Gilbert, E., Blettner, M., Gilbert, E., Habib, 

R. R., Malker, H., Muirhead, C. R., Richardson, D. B., Rogel, A., Schubauer-Berigan, M., 

Tardy, H., and Telle-Lamberton. 2008. Ionizing radiation and risk of chronic lymphocytic 

leukemia in the 15-country of nuclear industry workers. Radiat. Res. 170:661−665. 

Walsh, L., Rühm, W., and Kellerer, A. M. 2004. Cancer risk estimates for gamma-rays with 

regard to organ-specific doses. Part II: site specific solid cancers. Radiat. Environ. Biophys.

43:225−231. 

Weiss, H. A., Darby, C., and Doll, R. C. 1994. Cancer mortality following x-ray 

treatment for ankylosing spondylitis. Int. J. Cancer 59:327−338. 

Weiss, H. A., Darby, C., Fearn, T., and Doll, R. C. 1995. Leukemia mortality after x-ray 

treatment for ankylosing spondylitis. Radiat. Res. 142:1−11. 

Westermann, J., and Pabst, R. 1992. Distribution of lymphocyte subsets and natural killer cells in 

the human body. Clin. Investig. 70:539−544. 

Zablotska, L. B. 2004. Comments on the Arguments for Covering Chronic Lymphocytic 

Leukemia Under the Energy Employees Occupational Illness Compensation Program Act 

(EEOICPA) advanced by its stakeholders in the “Chronic Lymphocytic Leukemia: 

Reconsideration of Exclusion from Eligibility for Compensation under EEOICPA.” Report 

submitted to NIOSH/OCAS, December 2004. 

Young, A. J. 1999. The physiology of lymphocyte recirculation through the single lymph node in 

vivo. Seminars Immunol. 11:73−83. 

Young, A. J., Marston, W. L., Dessing, M., Dudler, L., and Hein, W. R. 1997. Distinct 
CLL Risk Model for NIOSH-IREP January 5, 2010 

 Final

 46 

recirculating and non-recirculating pools in the peripheral blood are defined by ccordinated 

expression of CD21 and L-selectin. Blood 90:4865−4875. 

Zent, C. S. 2007. OCAS NIOSH Report on CLL. Report submitted to NIOSH/OCAS, April 30, 

2007. 

ACKNOWLEDGEMENTS 

This study was funded by NIOSH under contract number 200-2006-18097. We thank David 

Richardson, Mary Schubauer-Berigan, Richard Wakeford, and Lydia Zablotska for their 

constructive review comments and helpful suggestions for improving the report. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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APPENDIX A 

AGE AND GENDER DEPENDENCE OF CHRONIC LYMPHOCYTIC 

LEUKEMIA (CLL) INCIDENCE RATES IN 

U.S. AND JAPANESE POPULATIONS 

 This appendix summarizes age-dependent and age-adjusted incidence rates of CLL (ICD-9 

204.1) in the United States (SEER 2005), in Japan, and in a combined population from 

Hiroshima and Nagasaki (from the IARC; Parkin et al. 1997). The age-adjusted rates are 

normalized to the world standard population, as is required for use in PC calculations in IREP. 

World-standardized cancer rates for the combined population from Hiroshima and Nagasaki are 

used in NIOSH-IREP to model the transfer of risk from the LSS cohort to the U.S. population. 

 The data in Tables A.1, A.2, A.3, and in Fig. A.1 show that there are large differences in 

background incidence rates of CLL in the U.S. and Japan. Incidence rates are more than an order 

of magnitude lower in the Japanese than in the U.S. population. The rates for Asians and Pacific 

Islanders who reside in the U.S. are also lower than for other population subgroups and are 

similar to those of the Japanese population, suggesting that the influence of genetic background 

may be stronger than that of environment on the incidence of CLL in Asians (see Section 3.4 in 

the main text). This conclusion holds not only for incidence rates of CLL alone, but also for rates 

of CLL + SLL. (Dores et al. 2007). 

 The data in the tables represent snapshots in time of expected incidence rates. Incidence rates 

have been changing over time for a variety of reasons. For example, Dores et al. (2007) showed 

that incidence rates compiled by the Surveillance, Epidemiology, and End Results (SEER) 

Program in the U.S. are affected by delays in reporting of CLL (but not of SLL). The ageadjusted rates reported by Dores et al. (2007) based on records covering 1993−2004 and 

standardized to the U.S. population in the year 2000 (5.34 per 105

 person-years for males and 

2.70 per 105

 person-years for females), which take this factor into account, are about 60% higher 

than the incidence rates shown in Table A.1. However, only a small portion of the 60% 

difference is due to the delay in reporting. The majority of this difference is due to the fact the 

age-adjusted rates reported by Dores et al. (2007) were obtained using the 2000 U.S. standard 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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population, while the age-adjusted rates included in this report were based on the world standard 

population. 

 Dores et al. (2007) showed that the incidence rate for SLL was about 1/3 that for CLL. They 

also reported what appeared to be differences in the gender and age-specific trends of SLL when 

compared to CLL. However, differences in methods of detection of CLL and SLL and lack of 

information from epidemiological studies prevented them from drawing firm conclusions about 

the meaning of their observations, i.e., without further investigation. 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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Table A.1. Incidence rates of CLL (ICD-9 204.1) 

in the United States based on SEER-13 data 

covering the 1992−2002 period.

CLL incidence rates in the U.S.a

[per 100,000 per year] 

Age group Male Females 

newborn 0 0.033

01-04 0 0.0082

05-09 0 0

10-14 0 0

15-19 0 0

20-24 0.0068 0.014

25-29 0.012 0.013

30-34 0.12 0.040

35-39 0.43 0.14

40-44 1.0 0.4

45-49 2.4 1.2

50-54 4.9 2.2

55-59 9.0 4.0

60-64 13.5 6.6

65-69 20.3 10.2

70-74 27.6 13.3

75-79 32.6 17.5

80-84 39.4 21.2

85+ 47.9 24.6

Ageadjustedb 3.3 (0.0375)c 1.6 (0.0239)c 

a

 SEER (2005) - SEER database: Incidence - SEER 13 Registries 

Public-Use, Nov 2004 Sub (1992-2002). 

b

 Adjusted to the world standard population. 

c

 Standard error in the age-adjusted rate. 
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Table A.2. Incidence rates of CLL (ICD-9 204.1) 

in the Japanese population. 

CLL incidence ratesa

[per 100,000 per year] 

Age group Male Females 

newborn 0 0 

01-04 0 0 

05-09 0 0 

10-14 0 0 

15-19 0 0.034 

20-24 0 0.038 

25-29 0.041 0 

30-34 0.035 0 

35-39 0.089 0 

40-44 0.139 0 

45-49 0.077 0.037 

50-54 0.46 0 

55-59 0.41 0.23 

60-64 0.90 0.11 

65-69 0.87 0.22 

70-74 0.87 0.37 

75-79 1.0 1.2 

80-84 2.6 0.68 

85+ 0 0 

Age-adjustedb 0.13 (0.016)c 0.037 (0.0078)c 

a

 Rates represent the combined population of all cancer registries 

in Japan – IARC Publication No. 143 (Parkin et al. 1997). 

b

 Adjusted to the world standard population. 

c

 Standard error in the age-adjusted rate. 
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Table A.3. Incidence rates of CLL (ICD-9 204.1) 

in the Hiroshima and Nagasaki population. 

CLL incidence ratesa

[per 100,000 per year] 

Age group Male Females 

newborn 0 0

01-04 0 0

05-09 0 0

10-14 0 0

15-19 0 0

20-24 0 0.23

25-29 0 0

30-34 0 0

35-39 0.19 0

40-44 0.56 0

45-49 0.48 0

50-54 0.52 0

55-59 0.27 0

60-64 0.61 0

65-69 0.84 0

70-74 2.94 0.40

75-79 0.80 0.51

80-84 0 2.32

85+ 5.11 0

Age-adjustedb 0.25 (0.055)c 0.043 (0.022)c 

a

 Rates are for the combined population of Hiroshima and 

Nagasaki, Japan – IARC Publication No. 143 (Parkin et al. 1997). 

b

 Adjusted to the world standard population. 

c

 Standard error in the age-adjusted rate. 

 

 
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0

10

20

30

40

50

60

MALES

CLL incidence rates (cases per 100,000 per yr)

0

10

20

30

40

50

60

00-04 05-0910-1415-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ 

FEMALES

US Population

Whites (US)

Blacks (US)

American Indians / Alaska Native (US)

Asian or Pacific Islanders (US)

Japanese Population

CLL incidence rates (cases per 100,000 per yr)

Age-group

Figure A.1. Comparison of incidence rates of CLL in different groups in U.S. and Japanese 

populations. Incidence data for the US population is obtained from SEER (1992-2002 dataset). 

Incidence data for the Japanese population is obtained from IARC (Parkin et al. 1997). 
CLL Risk Model for NIOSH-IREP January 5, 2010 

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APPENDIX B

ADDITIONAL INFORMATION BEARING ON ETIOLOGY OF CLL 

Models for the progression of many solid cancers from pre-malignant tumors to full-blown 

malignancies have been established, but the precursor lesions for lymphomas, including lowgrade forms such as CLL, have yet to be identified (Dewald et al. 2003; Braig et al. 2005). As 

assays have become more sensitive for detecting monoclonal B-CLL-like cells in peripheral 

blood, researchers have detected a monoclonal B-cell lymphocytosis (MBL) in about 3% of 

adults over 40 years of age with otherwise normal blood counts (<5000 lymphocytes mm-3) 

(Rawstron et al. 2002), but a higher percentage has been reported in families with an increased 

incidence of CLL (Rawstron et al. 2007). Currently however, the significance of these findings 

with respect to the etiology of CLL in the general population is not fully clear, because only a 

small percentage of those with detected MBL show progression to CLL, requiring treatment, 

while in others the MBL may spontaneously disappear or simply remain stable (Marti et al. 

2007; NCI 2009b). 

Molecular and cellular changes associated with the onset of CLL are being intensively 

investigated by research organizations around the world. A diverse set of B-cell genetic and cell 

surface markers has been identified and used to differentiate the indolent and aggressive 

subtypes of the disease (Stevenson et al. 1998; Stilgenbauer et al. 2002; Crespo et al. 2003; 

Dewald et al. 2003; Chiorazzi 2005; Chiorazzi 2007; Rai 2007b). It has been suggested that even 

B-cell CLL may not be a single disease because the clinical and genetic information indicate that 

median survival time post-diagnosis can range from a few months to >20 years, depending on the 

genetic characteristics, mutation status of the V genes, and expression of surface proteins, such 

as CD38 and ZAP70. Such characteristics often appear to correlate with disease progression and 

survival time (e.g., Stilgenbauer et al. 2002; Crespo et al. 2003; Dewald et al. 2003; Chiorazzi 

2005; NCI 2009b; Rai 2007b). 

B lymphocytes mature in the bone marrow and in the process rearrange immunoglobulin 

variable (V) genes to create the code for an immunoglobulin molecule that serves as the B-cell 
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receptor for antigen. When an antigen of adequate affinity engages the receptor, the cell typically 

enters a germinal center in lymph-node follicles, where it rapidly divides and its V genes 

undergo somatic hypermutation (see Fig. 2 in Chiorazzi et al. 2005). The somatic hypermutation 

process requires antigen stimulation, B-cell activation, cooperation of T lymphocytes and other 

cells, and, typically, the germinal center reaction (Keating et al. 2003). However, the process can 

occur without the involvement of T cells and outside germinal centers, in the marginal zones 

around lymphoid follicles (see Fig. 2 in Chiorazzi et al. 2005).14 

Both processes can lead to the development of plasma (antibody-secreting) cells or memory 

B cells.15 The latter retain a “memory” of the specific antigen that can be used to mobilize the 

immune system faster if the body encounters the antigen later in life. Although memory cells 

without V gene mutations that derive from T-cell-independent stimulation are often referred to as 

“antigen-experienced” B cells, to distinguish them from memory cells with somatic mutations 

acquired inside germinal centers (Chiorazzi et al. 2003), both types of cells are obviously 

“antigen-experienced” and “antigen-stimulated” (Fig. 2 in Chiorazzi et al. 2005; Table 1; see 

more below). 

Those B-CLL cells that have been mutated must have come from immunologically 

competent cells, but, using this parameter as the sole definition of immune competence, 

 “unmutated” B-CLL cells could derive from immature, naïve incompetent cells (i.e., possibly 

residing in the bone marrow). 

However, surface membrane phenotypic studies and measurements of telomeric shortening 

indicate that antigen stimulation and response to it are prerequisites for the development of BCLL, even in those cases that do not exhibit VH gene mutations. B-CLL cells over-express 

activation markers and under-express markers down-regulated by cell activation. Leukemic cells 

uniformly express CD27, identifying them as B cells that were triggered and had entered the 

memory (or plasma) cell pool. Telomere shortening occurs with each round of cell division. 

Telomeres of B-CLL cells are much shorter than those in normal B-cells, and those in 

 

14 Marginal zone B cells are so named because they reside in the marginal zones of the spleen and other lymphoid 

organs/tissues and are thought by many investigators to develop in the marginal zone of the spleen (Brown 1992; 

Kraal 1992; Weller et al. 2004; Pillai et al. 2005). In humans, they circulate and are found in lymph nodes, mucosalassociated lymphoid tissue, Peyer’s patches, and tonsils (Tierens et al. 1999; Pillai et al. 2005; Boyd 2007). 

15 Other potential CLL precursors are: mature B-cells residing in either the marginal zone or follicular mantle; the 

human equivalents of murine B-1 cells; V-preB+

L+ cells; and transitional B cells that routinely exit the marrow and 

circulate to solid lymphoid tissues (Chiorazzi et al. 2005; Hervé et al. 2005; Chiorazzi 2007). The first three of these 

groupings include some types of memory cells. 
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“unmutated” B-cells are much shorter than those in cells bearing VH gene mutations, suggesting 

that the “unmutated” leukemic cells have a much more extensive history of cell division than 

those in mutated cells (Keating et al. 2003; Rai 2007b). As noted above, rapid cell division takes 

place in the lymph-node follicle after antigen exposure. 

Whether they are mutated or not, current information indicates that B-CLL cells have passed 

through developmental stages that are not associated with the bone marrow, raising serious 

questions about its current designation as the most appropriate target organ for induction of CLL 

by radiation exposure. 
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APPENDIX C

ASSESSMENT OF POTENTIAL LATENCY FOR INCIDENCE OF CLL, 

LYMPHOMAS, AND MULTIPLE MYELOMA 

Our review of the epidemiological data indicated that the extended latency period (20+ years) 

for mortality from CLL suggested by Richardson et al. (2005) would not be applicable to a risk 

model for incidence of CLL (i.e., as in IREP). One reason is that the post-diagnosis survival time 

for CLL patients is highly uncertain, potentially ranging from a few months to a normal life 

expectancy (NCI 2009b). Another is that it is questionable whether existing epidemiological data 

on CLL induction by radiation are capable of providing useful information on an extended 

latency period (see Section 5.4 in main text). Four earlier studies in addition to that of Silver et 

al. (2007) potentially provide information on the latency period for radiogenic CLL: 

• Mortality in women with benign gynecological disease (mean age 46.5 y) treated with 

radiotherapy (mean dose to bone marrow: 1.2 Gy) (Inskip et al. 1993) (Table C.1). Fourteeen 

CLL cases were coded to ICD-8 (code 204.1), but then were lumped with 3 lymphocytic 

leukemias, not otherwise specified (code 204.9). 

• Mortality in ankylosing spondylitis patients (83.5% male) treated with radiotherapy (93.2% 

aged between 20−60 at first treatment; mean dose to bone marrow: 4.4 Gy) (Weiss et al. 

1995) (Table C.2). CLL deaths prior to 1979 were coded to ICD-8. ICD-9 codes were 

grouped to correspond with subtypes coded under ICD-8 (204.1, 207.8). 

• Incidence in cervical cancer patients (mean age 52 y) treated with radiotherapy (mean dose to 

bone marrow: 7.5 Gy) (Boice et al. 1985) (Table C.1). Mean age at leukemia diagnosis was 

in the range 60−64 y for acute lymphocytic leukemia and acute or chronic myeloid leukemia, 

but was 67 y for CLL (Boice et al. 1987). CLL = chronic and unspecified lymphatic leukemia 

(ICD-7 code 204.0); direct correspondences were made from ICD-8 to ICD-7 codes (Boice et 

al. 1985). 
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Table C.1. Risks for CLL plus unspecified lymphocytic leukemia by time since treatment in 

patients with cervical cancer (incidence) or benign gynecological disease (mortality) 

treated with radiotherapy 

Time since treatment (y) 

Source

0−9 10−19 20−29 >30 All years 

Observed 9 6 3 0 18 Boice et 

al. (1985) 

Expected 13.2 6.8 2.2 0.2 22.3 

SIR 0.7 0.9 1.4 0.0 0.8 

Observed 1 6 3 7 17 Inskip et 

al. (1993) 

Expected 1.08 2.06 2.90 3.19 9.23 

SMR 0.9 2.9 1.0 2.2 

(0.9−4.5)a

1.8 

(1.0−2.9)a

a

95% C.I.

Table C.2. Risks for mortality from CLL plus unspecified lymphocytic leukemia by time 

since treatment in ankylosing spondylitis patients treated with radiotherapy

Time since treatment (y) 

Source

0−4 5−14 15−24 >25 All years 

Observed 0 1 0 6 7 Weiss et 

al. (1995) 

SMR 0.0 1.8 0.0 2.0 

(0.7−4.3)a

1.4 

(0.6−2.8)a

Adjusted

SMR 

0.0 2.8 0.0 1.6 Not given 

a

95% C.I.

• Incidence in patients (79% female; mean age 47 y) exposed to I-131 for diagnostic or 

therapeutic (hyperthyroidism or thyroid cancer) procedures (Hall et al. 1992) (Tables C.3 and 

C.4). The mean doses to bone marrow varied by exposure group as follows: diagnostic (mean 

0.19 mGy), treatment for hyperthyroidism (48 mGy), and treatment for thyroid cancer 
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Table C.3. Incidence of CLLa plus unspecified lymphocytic leukemia by time since 

treatment in patients exposed to I-131 in diagnostic or therapeutic procedures

Time since treatment (y) Source

2−9 10−19 >20 All years 

Hall et al. (1992) 

Observed 13 30 22 65 

SIRb 0.71 

(0.38−1.21) 

1.22 

(0.83−1.75) 

1.26 

(0.79−1.90) 

1.08 

(0.84−1.38) a

Hall et al. (1992) do not specify which cancer classification was used in diagnosing CLL; 

however, because of the time frame in which the analyses were conducted, “CLL” may include 

unspecified lymphocytic leukemia (see text).

b

95% C.I. given in parentheses.

Table C.4. Incidence of CLLa

 in relation to dose to bone marrow in patients exposed 

to I-131 in diagnostic or therapeutic procedures 

Dose to bone marrow (and mean dose) (mGy) Source 

0.00−0.01 

(0.009) 

0.02−0.10 

(0.058) 

0.11−10 

(3.5) 

11−100 

(39) 

>100 

(221) 

Hall et 

al. 

Observed 2 17 30 9 7 (1992) b

SIRc 0.53 

(0.06−1.90) 

1.15 

(0.67−1.83) 

1.22 

(0.82−1.74) 

0.62 

(0.28−1.17) 

3.17 

(1.27−6.53) 

RRc 1.00d 2.22 

(0.52−9.63) 

2.54 

(0.61−10.6) 

1.17 

(0.25−5.41) 

6.49 

(1.34−31.3) a

Hall et al. (1992) do not specify which cancer classification was used in diagnosing CLL; 

however, because of the time frame in which the analyses were conducted, “CLL” may include 

unspecified lymphocytic leukemia (see text). 

b

4 of the 7 “CLL” cases occurred in patients treated for thyroid cancers (4 in 802 patients) vs 

3 cancers in 46,186 patients exposed to I-131 in diagnoses or treatment for hyperthyroidism. c

95% C.I. given in parentheses. 

d

Low-dose group was used as comparison group in estimating relative risk. 

(250 mGy). As might be expected, the highest incidence rate for “CLL” was in the latter group 

(see footnote b to Table C.4). No CLL classification scheme was referenced. 

Beyond the obvious concerns about potential for misdiagnoses and classification of CLL, all 

of the studies are potentially limited by the (1) confounding effects of prior disease on CLL 

incidence, (2) uncertainties in diagnosis and classification of CLL, (3) limited statistical power, 

particularly in the last case, because of the low doses involved, and (4) meaningfulness of bone 
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marrow doses for lymphoma induction. However, the epidemiological studies of mortality cited 

by Richardson et al. (2005) as providing the best evidence for an extended latency period 

[studies of patients treated for benign gynecological disorders (Inskip et al. 1993) or ankylosing 

spondylitis (Weiss et al. 1995)] have another major drawback for our purposes (i.e., beyond the 

fact that the numbers of “CLL” cases in the exposed groups are small and thus none of the results 

indicate an excess risk significantly different from zero) (Tables C.1 and C.2). Namely, the 

clinical and genetic information on CLL indicate that median survival time post-diagnosis can 

range anywhere from a few months to >20 years, depending on the genetic characteristics of the 

malignant cells involved , but with an estimated median survival time of ~10 y (e.g., 

Stilgenbauer et al. 2002; Crespo et al. 2003; Dewald et al. 2003). 

As Richardson et al. (2005) acknowledge, patients with CLL can live many years without 

developing significant symptoms, and often die with the disease, but from causes other than CLL 

(e.g., Kyasa et al. 2004). Thus, although the two epidemiological studies cited could contain 

evidence of an extended latency with respect to mortality (assuming that CLL was the 

predominant cause of mortality), the results would still have to be adjusted for estimated survival 

post-diagnosis to be useful for estimating a latency period for CLL incidence. 

The evidence for an extended latent period is weak in the results from the study by Inskip et 

al. (1993), where 41% of the cases of mortality attributed to “CLL” were observed in the first 20 

years, even if it is assumed that the grouped lymphocytic malignancies analyzed in the study 

were dominated by CLL (Table C.1). Although the trend appears to be stronger in the data from 

the study by Weiss et al. (1995), where 86% of the deaths occurred after 25 or more years had 

elapsed (Table C.2), the case for a significant effect is weakened by the very small numbers 

involved (7 total cases). 

If a median survival time post-diagnosis of 10 y is subtracted from the results for latency 

with respect to mortality in these two studies (i.e., a time since treatment of 20−29 y becomes 

10−19 y), an estimate for the mean latency period appears to fall in the range ~10−20 y (Table 

C.5). Even if the median survival time was skewed to a much lower value (say, 2 y), on the 

assumption that the “CLL” cases in these two studies were all more aggressive and thus led to 

earlier mortality than expected or that median survival time during the periods that the studies 

were conducted was much lower than reported, the results, when combined with the “CLL” 
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Table C.5. Adjusted numbers of CLL cases in patients treated with radiotherapy as a 

function of treatment condition and time since treatment 

Reason for radiotherapy

Time since treatment (y) 

0−9 10−19 ≥20 

Cervical cancer 9 6 3 

Benign gynecological disease 7a 3a 7a

Ankylosing spondylitis 1a 2b 4b

Thyroid diseases 13 30 22 

Total including thyroid diseases 30 41 36 

Total excluding thyroid diseases 17 11 14 a

Adjusted by moving original time since treatment range forward by one decade to reflect 

estimated time of incidence. 

b

Moving original time since treatment range forward by one decade to estimate time of 

incidence results in 6 cases observed at ≥15 y. These were apportioned between the decade 

10−19 y and all decades >20 y, as shown. 

incidence data from the studies by Boice et al. (1985) and Hall et al. (1992), still lead to the same 

conclusion: a mean latency ~10−20 y (see Tables C.1 and C.2). 

The results on incidence of “CLL” from the studies by Boice et al. (1985) of second cancers 

in cervical cancer patients and by Hall et al. (1992) of patients exposed to I-131 in diagnostic or 

therapeutic procedures both suggest that peak incidence occurs before 20 y has elapsed. Eightythree percent of “CLL” cases in cervical cancer patients and 66% of cases in patients exposed to 

I-131 were recorded within 20 years of treatment (Tables C.1 and C.3). Both of these studies 

have their drawbacks (effects of previous cancer in both, and relatively high doses in the first 

study, and very low doses and lower statistical power in the second case). On the other hand, the 

study by Hall et al. (1992) showed the greatest effect at the highest doses (Table C.4), albeit with 

large uncertainties and with perhaps the greatest potential for confounding (i.e., by thyroid 

cancer) . 

Taken as a whole, the limited data on the latency period for CLL induction by radiation 

exposure do not support the contention that the latency period is in excess of 20 years, but 
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instead indicate that the uncertainty associated with defining a latency period is quite large, even 

when the data for thyroid diseases are excluded (i.e., because radiation doses to this cohort were 

so low). However, even using our admittedly crude approach, it appears possible to make a 

reasonable case for a peak latency occurring prior to 20 y (28 total cases over 0−19 y vs 14 

occurring at or beyond 20 y), if the assumption is made that the lymphocytic malignancies 

identified in the other three studies were predominantly made up of CLL alone (ICD-9 code 

204.1). 

A question that immediately arises, however, is whether the latency period for the grouping 

of lymphoma and multiple myeloma [which include NHL, Hodgkin’s disease (lymphoma), and 

multiple myeloma; ICD-9 codes 200−203)] selected as a surrogate for CLL is similar to that of 

CLL. If it is not, it is conceivable that the risk coefficients in the IREP risk model (which was 

based on data on cancer incidence in individuals with a much wider age range than those treated 

with radiotherapy) could be affected. 

Data bearing on latency of the cancer types in the lymphoma and multiple myeloma 

grouping was available from three of the four epidemiological studies discussed above (Tables 

C.6 and C.7). The data for incidence of NHL and multiple myeloma as a function of time since 

treatment do not indicate a prolonged latency period; 92% and 80% of cases, respectively, were 

diagnosed in the first 19 y post-treatment (Table C.6). The mortality data are somewhat divergent 

(Tables C.6 and C.7). While both studies of mortality suggest the possibility of an extended 

latency period for multiple myeloma, one (Weiss et al. 1994) indicates that the peak latency for 

both NHL and Hodgkin’s lymphoma occurs before 25 y has elapsed, the other (Inskip et al. 

1993) shows no clear pattern for Hodgkin’s lymphoma (50% of cases diagnosed before and after 

20 years have elapsed, respectively), and the data for NHL in Table C.6 are not comparable to 

the data in Table C.7 because information on latency for 10 cases of NHL with ICD-8 code 202 

were not included in the former. 

Even with these limitations, when the mortality data are adjusted by subtracting the 

estimated time from diagnosis to mortality following treatment (as was done for CLL) to 

estimate the incidence patterns as a function of time and then are pooled with the incidence data 

(Table C.8), the results for both multiple myeloma and NHL are similar to those for CLL 

(particularly for the case where the data for thyroid diseases were excluded; see Table C.5). The 

results for Hodgkin’s lymphoma suggest a mean latency on the order of 10 y or less (Table C.8). 
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Table C.6. Numbers of cases of non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, and 

multiple myeloma by time since treatment in patients with cervical cancer (incidence) 

or benign gynecological disease (mortality) treated with radiotherapy 

Cancer site 

(ICD-8 code) 

Time since treatment (y)

0−9 10−19 20−29 ≥30 Source

All 

years 

Non-Hodgkin’s 

 lymphoma 

 (200, 202) 

34 26 5 0 65 Cervical cancer study 

 (Boice et al. 1985) 

Multiple 

 myeloma 

 (203) 

13 15 5 2 35 

Non-Hodgkin’s 

 lymphoma 

 (200 only) 

0 3 11 10 24 Benign gynecological 

 disease study (Inskip 

Hodgkin’s et al. 1993) 

 lymphoma 

 (201) 

2 3 2 3 10 

Multiple 

 myeloma 

 (203) 

1 1 8 4 14 

Table C.7. Numbers of deaths from non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, and 

multiple myeloma by time since treatment in ankylosing spondylitis patients 

treated with radiotherapy

Cancer site (ICD-9 code) 

Time since treatment (y) 

5−24 Source a >25 All years 

Non-Hodgkin’s lymphoma 

 (200, 202, exc. 202.4) 23 14 37 Weiss et 

Hodgkin’s lymphoma (201) al. (1994) 

11 2 13 

Multiple myeloma 

 (203, exc. 203.1, 238.6) 8 14 22 a

Five-year latency period was assumed. 

Since Hodgkin’s lymphoma contributed only 10 cases and NHL and multiple myeloma 

contributed 76 and 31, respectively, of the 117 cases in the combined set of data used to 

parameterize the Lymphoma and multiple myeloma model in IREP (our surrogate for CLL), the 

potential for confounding because of potential differences in latency when compared to CLL 

may not be a serious issue. However, the midpoint of the latency function used with the 
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Table C.8. Adjusted numbers of NHL, Hodgkin’s lymphoma, and multiple myeloma cases 

in patients treated with radiotherapy as a function of treatment condition 

and time since treatment 

Cancer site Reason for radiotherapy 

Time since treatment (y) 

0−9 10−19 20−29 ≥30 

Non-Hodgkin’s lymphoma 

Cervical cancer 34 26 5 0 

Benign gynecological diseasea 1b 7b 8b 8b

Ankylosing spondylitisa 10c 13c 7c 7c

Hodgkin’s lymphoma 

Benign gynecological disease 5 2 1d 2d

Ankylosing spondylitisa 7e 4e 1e 1e

Multiple myeloma 

Cervical cancer 13 15 5 2 

Benign gynecological diseasea 1f 2f 7f 4f

Ankylosing spondylitisa 2g 6g 5g 9g

Non-Hodgkin’s lymphoma 

All conditions 44 46 20 15 

Hodgkin’s lymphoma 

All conditionsh 12 6 2 3 

Multiple myeloma 

All conditions 16 23 17 15 a

Mortality studies. 

b

Moving original time-since-treatment range forward by 5 y to estimate time of incidence 

results in 3 cases observed at 5−14 y, 11 cases observed at 15−24 y, and 10 cases observed at 

≥25 y. These were apportioned between the decades 0−9 y, 10−19 y, 20−29 y and all decades 

≥30 y, as shown. c

Moving original time since treatment range forward by one decade to estimate time of 

incidence results in 23 cases observed at 0−19 y and 14 at ≥20 y. These were apportioned 

between the decades 0−9 y, 10−19 y, 20−29 y, and all decades ≥30 y, as shown. 

d

Moving original time since treatment range forward by one decade to estimate time of 

incidence results in 3 cases observed at ≥20 y. These were apportioned between the decade 

20−29 y and all decades >30 y, as shown. 

e

Moving original time since treatment range forward by one decade to estimate time of 

incidence results in 11 cases observed at 0−14 y and 2 at ≥15 y. These were apportioned between 

the decades 0−9 y, 10−19 y, and all decades ≥20 y, as shown. 
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______________________________Table C.8 (continued)_____________________________

f

Moving original time since treatment range forward by 3 y to estimate time of incidence 

results in 1 case observed at 0−6 y, 1 case observed at 7−16 y, 8 cases observed at 17−26 y, and 

4 cases observed at ≥27 y. These were apportioned between the decades 0−9 y, 10−19 y, 20−29 

y, and all decades ≥30 y, as shown. 

g

Moving original time since treatment range forward by 3 y to estimate time of incidence 

results in 8 cases observed at 2−21 y and 14 at ≥22 y. These were apportioned between the 

decades 0−9 y, 10−19 y, 20−29 y, and all decades ≥30 y, as shown. 

h

Data not available for benign gynecological disease study. 

lymphoma and multiple myeloma model in IREP is the same as that for solid tumors, and thus 

some modification appears to be justified for its use with a CLL model. 

Another set of data that might provide information bearing on the issue of latency (or age 

dependencies on risks) is that for the Japanese atomic-bomb survivors (Table C.9). For 

individuals exposed at ages ≥40 (and at doses ≥0.01 Gy), the time-dependent incidence of NHL 

combined with Hodgkin’s lymphoma as is reasonably similar to that for CLL in radiotherapy 

patients (Table C.5), who were of similar ages when exposed. However, the data for individuals 

exposed at younger ages and data for incidence of multiple myeloma for all ages at exposure 

indicate that most cases were diagnosed more than 25 y after the bombings. These observations 

could indicate a longer latency for lymphoma in individuals exposed at younger ages or at all 

ages at exposure for those diagnosed with multiple myeloma. However, because the patterns for 

individuals exposed at doses ≥0.01 Gy and at doses <0.01 Gy are so similar, these data probably 

result from the increase in incidence of CLL with increasing age (see Appendix A), and could be 

interpreted to mean that radiation exposure had little influence on development of their 

lymphomas. This is an issue that could be reexamined at some point, perhaps in conjunction with 

the reanalysis of the LSS data when the DS02-based dose-response information becomes 

available. 

The only published atomic-bomb survivor data that include CLL are compromised because 

CLL is combined with HCL, acute leukemias of unspecified type, and cases of myelodysplastic 

syndrome (Table C.10). The small numbers involved also limit interpretation of the data, but, in 

general, the time-dependent incidence of these “other” leukemias is very similar to that for NHL 

and Hodgkin’s lymphoma in Table C.9. 
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Table C.9. Number of lymphoma and multiple myeloma cases in Japanese atomic bomb 

survivors as a function of time since the bombingsa

Cancer site 

Age at 

exposure (y) 

Exposure 

(Gy) 

Time since bombings (y) 

5−7 8−12 13−24 25−32 

NHL and Hodgkin’s 

 lymphoma 0−19 ≥0.01 0 3 2 12 

<0.01 0 0 9 11 

20−39 ≥0.01 0 1 10 25 

<0.01 3 2 10 25 

≥40 ≥0.01 0 5 17 11 

<0.01 1 6 17 1 

Multiple myeloma 

0−19 ≥0.01 0 0 2 3 

<0.01 0 0 0 2 

20−39 ≥0.01 0 1 2 6 

<0.01 0 0 1 9 

≥40 ≥0.01 0 0 5 11 

<0.01 0 0 1 15 a

Source: Table III in Preston et al. (1994). 
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 C.10. Number of “other leukemia” cases in Japanese atomic bomb survivors from 

Hiroshima as a function of time since the bombingsa

Age at exposure (y) Exposure (Gy) 

Time since bombings (y) 

5−20 21−32 

0−19 ≥0.01 1 1 

<0.01 1 0 

20−39 ≥0.01 0 4 

<0.01 2 2 

≥40 ≥0.01 1 1 

<0.01 2 0 a

Source: Table XI in Preston et al. (1994). “Other leukemia” cases include: 4 cases of CLL, 2 of 

HCL, 7 of acute leukemias of unspecified type, and 2 of myelodysplastic syndrome. They are 

based only on Hiroshima data because cases from Nagasaki were predominantly adult T-cell 

leukemia, for which no association with radiation exposure was found.