{'fraction_non_alphanumeric': 0.049233567160301375, 'fraction_numerical': 0.02247336970641725, 'mean_word_length': 4.736959761549925, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Thyroid Cancer Following Childhood Low-Dose Radiation Exposure: A Pooled Analysis of Nine Cohorts

Context: The increased use of diagnostic and therapeutic procedures that involve radiation raises concerns about radiation effects, particularly in children and the radiosensitive thyroid gland. Objectives: Evaluation of relative risk (RR) trends for thyroid radiation doses <0.2 gray (Gy); evidence of a threshold dose; and possible modifiers of the dose-response, e.g., sex, age at exposure, time since exposure. Design and Setting: Pooled data from nine cohort studies of childhood external radiation exposure and thyroid cancer with individualized dose estimates, ≥1000 irradiated subjects or ≥10 thyroid cancer cases, with data limited to individuals receiving doses <0.2 Gy. Participants: Cohorts included the following: childhood cancer survivors (n = 2); children treated for benign diseases (n = 6); and children who survived the atomic bombings in Japan (n = 1). There were 252 cases and 2,588,559 person-years in irradiated individuals and 142 cases and 1,865,957 person-years in nonirradiated individuals. Intervention: There were no interventions. Main Outcome Measure: Incident thyroid cancers. Results: For both <0.2 and <0.1 Gy, RRs increased with thyroid dose (P < 0.01), without significant departure from linearity (P = 0.77 and P = 0.66, respectively). Estimates of threshold dose ranged from 0.0 to 0.03 Gy, with an upper 95% confidence bound of 0.04 Gy. The increasing dose–response trend persisted >45 years after exposure, was greater at younger age at exposure and younger attained age, and was similar by sex and number of treatments. Conclusions: Our analyses reaffirmed linearity of the dose response as the most plausible relationship for “as low as reasonably achievable” assessments for pediatric low-dose radiation-associated thyroid cancer risk.


Source and organization of data
We described data acquisition in (1). For this analysis, we restricted data to doses <0.2 gray (Gy) and <0.1 Gy, resulting in the pooling of nine cohort studies with three diverse radiation sources, including treatment for cancer, treatment for benign diseases (tinea capitis, enlarged thymus, hemangioma and enlarged tonsils and adenoids) and Japanese atomic bomb survivors. Cases were those who developed incident thyroid cancer during follow-up, excluding non-melanoma skin cancer. For studies of benign diseases and atomic bomb survivors, thyroid cancer represented the first primary cancer, while for the cancer survivor cohorts, it represented the second primary cancer. For comparability across studies, we censored autopsy-identified thyroid cancers at date of death, but did not include them as cases.
There were minor differences in follow-up, numbers of patients and thyroid cancer cases in the pooled data from the individual studies in original publications (1). For most studies, person-time accrual started at first radiation exposure or at enrollment for nonexposed and continued until the earliest date of death, loss to follow-up, incident cancer or end of study. For the Atomic Bomb Survivors study, follow-up and case accrual started in 1958, 13 years after exposure. For the Childhood Cancer Survivor Study (CCSS) cohorts, follow-up started five years (CCSS-US) or three years (CCSS-Fr/UK) after first cancer (Appendix Table 1).
There were 142 non-exposed and 252 radiation-exposed thyroid cancer cases that accrued in 1,865,957 and 2,588,559 person-years of follow-up, respectively, for subjects with doses <0.2 Gy (Appendix Table 1). For doses <0.1 Gy, in the radiation-exposed, there were 184 cases and 2,114,683 person-years of follow-up.

Models for thyroid cancer risk
We modeled the thyroid cancer incidence rate, r( , , ), using a vector of explanatory variables, x, which varied with analysis but generally included study, sex, attained age, year of birth and other factors, an indicator variable for CCSS patients with chemotherapy exposure, c, and thyroid radiation dose, d. Previous analysis determined that an additive model best described the joint association of radiation exposure and treatment with chemotherapy (c =1 if yes and c =0 if no). In an additive relationship for two factors (radiation dose and chemotherapy), often termed non-synergistic, the incidence rate for exposure to both factors, r( , , ), equals the sum of the incidence rate in the absence of both factors, r (  We rewrote the additive model as where r 0 (x)=r( , 0, 0)=exp(ϕ x) was the thyroid cancer incidence rate among non-radiation, non-chemotherapy exposed individuals with ϕ a vector of parameters, ERR(d) was the radiation-associated excess relative risk (ERR) and θ was the excess relative risk of chemotherapy. For doses <0.2 Gy, we fitted a linear model, ERR(d) = β d, to the data and examined departures from linearity using power r(x, d, c) = r 0 (x) {1 + β + } and linear-exponential forms; results were similar and we reported P-values only for the former. We computed a likelihood-based 95% confidence interval (CI) for estimates of β.
A threshold dose designates a dose below which there is no apparent radiation effect. We conducted a profile search to find the maximum likelihood estimate of the threshold under a linear model, namely, ERR(d) = β ( − ) + , where ( − ) + = max (0, − ) with η the unknown threshold. Finally, we also employed a less restrictive semi-parametric modeling approach and fitted a restricted cubic spline with 4-knots with knots identified using the AIC and a grid search (15,16).
For a categorical effect modifier z with J levels,(∑ ) replaced β, where zj was an indicator variable and βj was the ERR/Gy for the j th category, i.e., We evaluated sex, age at radiation exposure, attained age, time since exposure and number of radiation treatments as potential modifiers of dose effects and evaluated effect modification with a likelihood ratio tests which compared deviances for Appendix equations 1 and 2. We also fitted models that included continuous modifiers, i.e., ERR(d, z) = β d g(z), where we identified the best fitting model using ln{g(.)} and , 2 , ln( ) and ln ( ) 2 .
The best-fitting model across the full range of radiation doses in excess of 70 Gy was the following (1): We fitted equation 3 to the nine studies using all doses to compute downward extrapolations for comparison of fitted RRs at 0.2 Gy with estimates from models that we developed in restricted data. We note that equation 3 embedded a linear, 1 = 2 = 3 = 0 model.
We smoothed the log-RRs in Figure 1 using a procedure similar to Pierce and Preston (17). For a 5-point moving average, we used the log RRs with prior weights of 0.10, 0.25, 0.30, 0.25, 0.10 and used the covariance matrix with weights assumed fixed to obtain standard errors. Table 2 provides the fitted relative risk (RR) at 0.2 Gy overall and for categories of various potential effect modifiers. Age at first exposure, time since exposure and attained age were of particular note. Appendix Figure 1 shows the category-specific fitted estimates at 0.2 Gy (solid symbol) and fitted models using continuous variables for the modifiers (solid line). Appendix Figure 1: Fitted relative risks (RR) at 0.2 Gy based on a linear excess RR model with additive adjustment for use of chemotherapy accounting for effect modification by categories of age in years at first radiation exposure, years since first radiation exposure and attained age in categories (solid symbol) (RR0.2 Gy in main text Table 2 with 95% CI) and with a best fitting model based on the Akaike Information Criterion using continuous variables (solid line). Data pooled from nine cohort studies and limited to <0.2 Gy.