Deukwoo Kwon

University of Miami, كورال غيبلز، فلوريدا, Florida, United States

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Publications (29)53.81 Total impact

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    ABSTRACT: Dosimetic uncertainties, particularly those that are shared among subgroups of a study population, can bias, distort or reduce the slope or significance of a dose response. Exposure estimates in studies of health risks from environmental radiation exposures are generally highly uncertain and thus, susceptible to these methodological limitations. An analysis was published in 2008 concerning radiation-related thyroid nodule prevalence in a study population of 2,994 villagers under the age of 21 years old between August 1949 and September 1962 and who lived downwind from the Semipalatinsk Nuclear Test Site in Kazakhstan. This dose-response analysis identified a statistically significant association between thyroid nodule prevalence and reconstructed doses of fallout-related internal and external radiation to the thyroid gland; however, the effects of dosimetric uncertainty were not evaluated since the doses were simple point "best estimates". In this work, we revised the 2008 study by a comprehensive treatment of dosimetric uncertainties. Our present analysis improves upon the previous study, specifically by accounting for shared and unshared uncertainties in dose estimation and risk analysis, and differs from the 2008 analysis in the following ways: 1. The study population size was reduced from 2,994 to 2,376 subjects, removing 618 persons with uncertain residence histories; 2. Simulation of multiple population dose sets (vectors) was performed using a two-dimensional Monte Carlo dose estimation method; and 3. A Bayesian model averaging approach was employed for evaluating the dose response, explicitly accounting for large and complex uncertainty in dose estimation. The results were compared against conventional regression techniques. The Bayesian approach utilizes 5,000 independent realizations of population dose vectors, each of which corresponds to a set of conditional individual median internal and external doses for the 2,376 subjects. These 5,000 population dose vectors reflect uncertainties in dosimetric parameters, partly shared and partly independent, among individual members of the study population. Risk estimates for thyroid nodules from internal irradiation were higher than those published in 2008, which results, to the best of our knowledge, from explicit accounting for dose uncertainty. In contrast to earlier findings, the use of Bayesian methods led to the conclusion that the biological effectiveness for internal and external dose was similar. Estimates of excess relative risk per unit dose (ERR/Gy) for males (177 thyroid nodule cases) were almost 30 times those for females (571 cases) and were similar to those reported for thyroid cancers related to childhood exposures to external and internal sources in other studies. For confirmed cases of papillary thyroid cancers (3 in males, 18 in females), the ERR/Gy was also comparable to risk estimates from other studies, but not significantly different from zero. These findings represent the first reported dose response for a radiation epidemiologic study considering all known sources of shared and unshared errors in dose estimation and using a Bayesian model averaging (BMA) method for analysis of the dose response.
    Radiation Research 01/2015; 183(2). DOI:10.1667/RR13794.1 · 2.45 Impact Factor
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    ABSTRACT: Simon, S. L., Preston, D. L., Linet, M. S., Miller, J. S., Sigurdson, A. J., Alexander, B. H., Kwon, D., Yoder, R. C., Bhatti, P., Little, M. P., Rajaraman, P., Melo, D., Drozdovitch, V., Weinstock, R. M. and Doody, M. M. Radiation Organ Doses Received in a Nationwide Cohort of U.S. Radiologic Technologists: Methods and Findings. Radiat. Res. 182, 507–528 (2014). In this article, we describe recent methodological enhancements and findings from the dose reconstruction component of a study of health risks among U.S. radiologic technologists. An earlier version of the dosimetry published in 2006 used physical and statistical models, literature-reported exposure measurements for the years before 1960, and archival personnel monitoring badge data from cohort members through 1984. The data and models previously described were used to estimate annual occupational radiation doses for 90,000 radiological technologists, incorporating information about each individual’s employment practices based on a baseline survey conducted in the mid-1980s. The dosimetry methods presented here, while using many of the same methods as before, now estimate 2.23 million annual badge doses (personal dose equivalent) for the years 1916–1997 for 110,374 technologists, but with numerous methodological improvements. Every technologist’s annual dose is estimated as a probability density function to reflect uncertainty about the true dose. Multiple realizations of the entire cohort distribution were derived to account for shared uncertainties and possible biases in the input data and assumptions used. Major improvements in the dosimetry methods from the earlier version include: A substantial increase in the number of cohort member annual badge dose measurements; Additional information on individual apron usage obtained from surveys conducted in the mid-1990s and mid-2000s; Refined modeling to develop lognormal annual badge dose probability density functions using censored data regression models; Refinements of cohort-based annual badge probability density functions to reflect individual work patterns and practices reported on questionnaires and to more accurately assess minimum detection limits; and Extensive refinements in organ dose conversion coefficients to account for uncertainties in radiographic machine settings for the radiographic techniques employed. For organ dose estimation, we rely on well-researched assumptions about critical exposure-related variables and their changes over the decades, including the peak kilovoltage and filtration typically used in conducting radiographic examinations, and the usual body location for wearing radiation monitoring badges, the latter based on both literature and national recommendations. We have derived organ dose conversion coefficients based on air-kerma weighting of photon fluences from published X-ray spectra and derived energy-dependent transmission factors for protective lead aprons of different thicknesses. Findings are presented on estimated organ doses for 12 organs and tissues: red bone marrow, female breast, thyroid, brain, lung, heart, colon, ovary, testes, skin of trunk, skin of head and neck and arms, and lens of the eye.
    Radiation Research 11/2014; 182(5):507-528. DOI:10.1667/RR13542.1 · 2.45 Impact Factor
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    ABSTRACT: Most conventional risk analysis methods rely on a single best estimate of exposure per person which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2,376 subjects following exposure to fallout resulting from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulation tests and comparisons against conventional regression risk analysis methods. We conclude that when estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple realizations of possibly true dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods as well as a markedly increased capability to include the pre-established 'true' risk coefficient within the credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure.
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    Radiation Research 09/2014; 182(3):e44. DOI:10.1667/RROL09.1 · 2.45 Impact Factor
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    ABSTRACT: Little, M.P., Kwon, D., Doi, K., Simon, S.L., Preston, D.L., Doody, M.M., Lee, T., Miller, J.S., Kampa, D.M., Bhatti, P., Tucker, J.D., Linet, M.S., Sigurdson, A.J., Association of Chromosome Translocation Rate with Low Dose Occupational Radiation Exposures in U.S. Radiologic Technologists. Radiat. Res. 182, 1-17 (2014). Chromosome translocations are a well-recognized biological marker of radiation exposure and cancer risk. However, there is uncertainty about the lowest dose at which excess translocations can be detected, and whether there is temporal decay of induced translocations in radiation-exposed populations. Dosimetric uncertainties can substantially alter the shape of dose-response relationships; although regression-calibration methods have been used in some datasets, these have not been applied in radio-occupational studies, where there are also complex patterns of shared and unshared errors that these methods do not account for. In this paper we evaluated the relationship between estimated occupational ionizing radiation doses and chromosome translocation rates using fluorescent in situ hybridization in 238 US radiologic technologists selected from a large cohort. Estimated cumulative red-bone-marrow doses (mean 29.3 mGy, range 0-135.7 mGy) were based on available badge-dose measurement data and on questionnaire-reported work-history factors. Dosimetric assessment uncertainties were evaluated using regression-calibration, Bayesian, and Monte-Carlo maximum-likelihood methods, taking account of shared and unshared error, and adjusted for overdispersion. There was a significant dose response for estimated occupational radiation exposure, adjusted for questionnaire-based personal diagnostic radiation, age, sex, and study group (5.7 translocations per 100 whole-genome cell equivalents per Gy, 95% CI 0.2, 11.3, p=0.0440). A significant increasing trend with dose continued to be observed for individuals with estimated doses <100 mGy. For combined estimated occupational and personal diagnostic medical radiation exposures, there was a borderline-significant modifying effect of age (p=0.0704), but little evidence (p>0.5) of temporal decay of induced translocations. The three methods of analysis to adjust for dose uncertainty gave similar results. In summary, chromosome translocation dose-response slopes were detectable down to <100 mGy, and were compatible with those observed in other radiation-exposed populations. However, there are substantial uncertainties in both occupational and other (personal diagnostic medical) doses that may be imperfectly taken into account in our analysis.
    Radiation Research 06/2014; 182(1):1-17. DOI:10.1667/RR13413.1 · 2.45 Impact Factor
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    ABSTRACT: The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged <18 at the time of the accident who were resident in the most radioactively contaminated regions of Ukraine. We extended earlier analyses in this cohort by adjusting for dose error in the recently developed TD-10 dosimetry. Three methods of statistical correction, via two types of regression calibration, and Monte Carlo maximum-likelihood, were applied to the doses that can be derived from the ratio of thyroid activity to thyroid mass. The two components that make up this ratio have different types of error, Berkson error for thyroid mass and classical error for thyroid activity. The first regression-calibration method yielded estimates of excess odds ratio of 5.78 Gy(-1) (95% CI 1.92, 27.04), about 7% higher than estimates unadjusted for dose error. The second regression-calibration method gave an excess odds ratio of 4.78 Gy(-1) (95% CI 1.64, 19.69), about 11% lower than unadjusted analysis. The Monte Carlo maximum-likelihood method produced an excess odds ratio of 4.93 Gy(-1) (95% CI 1.67, 19.90), about 8% lower than unadjusted analysis. There are borderline-significant (p = 0.101-0.112) indications of downward curvature in the dose response, allowing for which nearly doubled the low-dose linear coefficient. In conclusion, dose-error adjustment has comparatively modest effects on regression parameters, a consequence of the relatively small errors, of a mixture of Berkson and classical form, associated with thyroid dose assessment.
    PLoS ONE 01/2014; 9(1):e85723. DOI:10.1371/journal.pone.0085723 · 3.53 Impact Factor
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    ABSTRACT: PURPOSE: To provide dosimetric data for an epidemiologic study on the risk of second primary esophageal cancer among breast cancer survivors, by reconstructing the radiation dose incidentally delivered to the esophagus of 414 women treated with radiation therapy for breast cancer during 1943-1996 in North America and Europe. METHODS AND MATERIALS: We abstracted the radiation therapy treatment parameters from each patient's radiation therapy record. Treatment fields included direct chest wall (37% of patients), medial and lateral tangentials (45%), supraclavicular (SCV, 64%), internal mammary (IM, 44%), SCV and IM together (16%), axillary (52%), and breast/chest wall boosts (7%). The beam types used were (60)Co (45% of fields), orthovoltage (33%), megavoltage photons (11%), and electrons (10%). The population median prescribed dose to the target volume ranged from 21 Gy to 40 Gy. We reconstructed the doses over the length of the esophagus using abstracted patient data, water phantom measurements, and a computational model of the human body. RESULTS: Fields that treated the SCV and/or IM lymph nodes were used for 85% of the patients and delivered the highest doses within 3 regions of the esophagus: cervical (population median 38 Gy), upper thoracic (32 Gy), and middle thoracic (25 Gy). Other fields (direct chest wall, tangential, and axillary) contributed substantially lower doses (approximately 2 Gy). The cervical to middle thoracic esophagus received the highest dose because of its close proximity to the SCV and IM fields and less overlying tissue in that part of the chest. The location of the SCV field border relative to the midline was one of the most important determinants of the dose to the esophagus. CONCLUSIONS: Breast cancer patients in this study received relatively high incidental radiation therapy doses to the esophagus when the SCV and/or IM lymph nodes were treated, whereas direct chest wall, tangentials, and axillary fields contributed lower doses.
    International journal of radiation oncology, biology, physics 04/2013; 86(4). DOI:10.1016/j.ijrobp.2013.03.014 · 4.18 Impact Factor
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    ABSTRACT: BACKGROUND: Epidemiologic studies have shown consistent associations between obesity and increased thyroid cancer risk, but, to date, no studies have investigated the relationship between thyroid cancer risk and obesity-related single nucleotide polymorphisms (SNPs). METHODS: We evaluated 575 tag SNPs in 23 obesity-related gene regions in a case-control study of 341 incident papillary thyroid cancer (PTC) cases and 444 controls of European ancestry. Logistic regression models, adjusted for attained age, year of birth, and sex were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) with SNP genotypes, coded as 0, 1, and 2 and modeled continuously to calculate P-trends. RESULTS: Nine out of 10 top-ranking SNPs (Ptrend<0.01) were located in the FTO (fat mass and obesity associated) gene region, while the other was located in INSR (insulin receptor). None of the associations were significant after correcting for multiple testing. CONCLUSIONS: Our data do not support an important role of obesity-related genetic polymorphisms in determining the risk of PTC. Impact: Factors other than selected genetic polymorphisms may be responsible for the observed associations between obesity and increased PTC risk.
    Cancer Epidemiology Biomarkers & Prevention 10/2012; 21(12). DOI:10.1158/1055-9965.EPI-12-0790 · 4.32 Impact Factor
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    ABSTRACT: The assessment of potential benefits versus harms from mammographic examinations as described in the controversial breast cancer screening recommendations of the U.S. Preventive Task Force included limited consideration of absorbed dose to the fibroglandular tissue of the breast (glandular tissue dose), the tissue at risk for breast cancer. Epidemiological studies on cancer risks associated with diagnostic radiological examinations often lack accurate information on glandular tissue dose, and there is a clear need for better estimates of these doses. Our objective was to develop a quantitative summary of glandular tissue doses from mammography by considering sources of variation over time in key parameters, including imaging protocols, X-ray target materials, voltage, filtration, incident air kerma, compressed breast thickness, and breast composition. We estimated the minimum, maximum and mean values for glandular tissue dose for populations of exposed women within 5-year periods from 1960 to the present, with the minimum to maximum range likely including 90% to 95% of the entirety of the dose range from mammography in North America and Europe. Glandular tissue dose from a single view in mammography is presently about 2 mGy, about one-sixth the dose in the 1960s. The ratio of our estimates of maximum to minimum glandular tissue doses for average-size breasts was about 100 in the 1960s compared to a ratio of about 5 in recent years. Findings from our analysis provide quantitative information on glandular tissue doses from mammographic examinations that can be used in epidemiological studies of breast cancer.
    Radiation Research 01/2012; 177(1):92-108. DOI:10.2307/41408650 · 2.45 Impact Factor
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    ABSTRACT: We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyper-parameters is assessed in simulation studies. We also examined performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and better identified the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a skin cancer.
    Computational Statistics & Data Analysis 10/2011; 55(10):2807-2818. DOI:10.1016/j.csda.2011.04.019 · 1.15 Impact Factor
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    ABSTRACT: To determine more accurate regression formulas for estimating peak skin dose (PSD) from reference air kerma (RAK) or kerma-area product (KAP). After grouping of the data from 21 procedures into 13 clinically similar groups, assessments were made of optimal clustering using the Bayesian information criterion to obtain the optimal linear regressions of (log-transformed) PSD vs RAK, PSD vs KAP, and PSD vs RAK and KAP. Three clusters of clinical groups were optimal in regression of PSD vs RAK, seven clusters of clinical groups were optimal in regression of PSD vs KAP, and six clusters of clinical groups were optimal in regression of PSD vs RAK and K AP. Prediction of PSD using both RAK andKAP is significantly better than prediction of PSD with either RAK or KAP alone. The regression of PSD vs RAK provided better predictions of PSD than the regression of PSD vs KAP. The partial-pooling (clustered) method yields smaller mean squared errors compared with the complete-pooling method. PSD distributions for interventional radiology procedures are log-normal. Estimates of PSD derived from RAK and KAP jointly are mos t accurate, followed closely byestimates derived from RAK alone. Estimates of PSD derived from KAP alone are the least accurate. Using a stochastic search approach, it is possible to cluster together certain dissimilar types of procedures to minimize the total error sum of squares.
    Medical Physics 07/2011; 38(7):4196-204. DOI:10.1118/1.3590358 · 3.01 Impact Factor
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    ABSTRACT: Abstract In this paper, we describe recent methodological enhancements and findings from the dose reconstruction component of a study of cancer risks among U.S. radiologic technologists. An earlier version of the dosimetry published in 2006 (Simon et al., Radiat. Res. 166, 174-192, 2006) used physical and statistical models, literature-reported exposure measurements for the years before 1960, and archival personnel monitoring badge data from cohort members through 1984. The data and models were used to estimate unknown occupational radiation doses for 90,000 radiological technologists, incorporating information about each individual's employment practices based on a survey conducted in the mid-1980s. The dosimetry methods presented here, while using many of the same methods as before, now estimate annual and cumulative occupational badge doses (personal dose equivalent) to about 110,000 technologists for each year worked from 1916 to 2006, but with numerous methodological improvements. This dosimetry, using much more comprehensive information on individual use of protection aprons, estimates radiation absorbed doses to 12 organs and tissues (red bone marrow, ovary, colon, brain, lung, heart, female breast, skin of trunk, skin of head and neck and arms, testes, thyroid and lens of the eye). Every technologist's annual dose is estimated as a probability density function (pdf) to account for shared and unshared uncertainties. Major improvements in the dosimetry methods include a substantial increase in the number of cohort member annual badge dose measurements, additional information on individual apron use obtained from surveys conducted in the 1990s and 2005, refined modeling to develop annual badge dose pdfs using Tobit regression, refinements of cohort-based annual badge pdfs to delineate exposures of highly and minimally exposed individuals and to assess minimal detectable limits more accurately, and extensive refinements in organ dose conversion coefficients to account for uncertainties in radiographic techniques employed. For organ dose estimation, we rely on well-researched assumptions about critical exposure-related variables and their changes over the decades, including the peak kilovoltage and filtration typically used in conducting radiographic examinations and the usual body location for wearing radiation monitoring badges. We have derived organ dose conversion coefficients based on air-kerma weighting of photon fluences from published X-ray spectra and derived energy-dependent transmission factors for protective aprons of different thicknesses. We tailor bone marrow dose estimates to individual cohort members by using an individual-specific body mass index correction factor. To our knowledge the models and reconstructed doses presented herein represent the most comprehensive dose reconstructions undertaken for a cohort of medical radiation workers.
    Radiation Research 05/2010; DOI:10.1667/RR2069.1 · 2.45 Impact Factor
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    Jeesun Jung, Joon Jin Song, Deukwoo Kwon
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    ABSTRACT: ABSTRACT : The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected.
    BMC proceedings 12/2009; 3 Suppl 7(Suppl 7):S76. DOI:10.1186/1753-6561-3-S7-S76

Publication Stats

258 Citations
53.81 Total Impact Points

Institutions

  • 2012–2015
    • University of Miami
      • Sylvester Comprehensive Cancer Center
      كورال غيبلز، فلوريدا, Florida, United States
    • NCI-Frederick
      Фредерик, Maryland, United States
  • 2006–2011
    • National Cancer Institute (USA)
      • • Division of Cancer Epidemiology and Genetics
      • • Radiation Epidemiology
      Maryland, United States
  • 2008–2010
    • National Institutes of Health
      • Division of Cancer Epidemiology and Genetics
      베서스다, Maryland, United States
  • 2007
    • Texas A&M University
      • Department of Statistics
      College Station, Texas, United States