Estimating Testicular Cancer specific Mortality by Using the Surveillance Epidemiology and End Results Registry

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1 Appendix E1 Estimating Testicular Cancer specific Mortality by Using the Surveillance Epidemiology and End Results Registry To estimate cancer-specific mortality for 33-year-old men with stage I testicular cancer, we used publicly available Surveillance Epidemiology and End Results *Stat software to query the Surveillance Epidemiology and End Results national cancer registry (13). We elicited data from a pooled range of patient ages at diagnosis, from 23 to 43 years, to increase the sample size for estimates obtained (13). International Classification of Disease for Oncology, 3rd edition (or ICD-O-3) codes were used to identify cases of seminoma ( ) and NSGCT ( ) (13). We restricted cases to those in which a diagnosis was made from 1993 to 2003 to ensure adequate longitudinal follow-up and because inclusion of cases prior to and following 2003 would have led to a heterogeneous case mix due to changes in extent-of-disease coding that occurred after 2003 (13). The primary result of our approach was that patients with both normal and elevated tumor markers after orchiectomy were included in our analysis. Importantly, imaging surveillance (without empiric adjuvant therapy) remains an appropriate management option regardless. Extent-of-disease codes were selected to match stage I cancers and included 10, 15, 20, 30, 40, 45, 50, 60, and 70 for both seminoma and NSGCT (13). Cases in which patients had nodal or distant metastases were excluded. In total, 4146 patients constituted the patient group for seminoma, and 1882 patients constituted that for NSGCT. We extracted 5-year cause-specific survival estimates with 95% confidence intervals, computed by using Kaplan-Meier methods, and converted them to mortality rates by using standard exponential assumptions, as detailed in the article (13,14). Estimating Radiation-induced Cancer Risks in Multiple Organ Systems Radiation-induced Cancer Mortality We modeled cancer mortality risks for 13 solid organs (lung, esophagus, stomach, pancreas, liver, colon, rectum, kidney, bladder, prostate, central nervous system, thyroid, and oral cavity) and for leukemia (6). For the majority of organ systems considered in our analysis, we computed radiation-induced cancer mortality rates from a geometric mean of excess absolute risk (EAR) and excess relative risk (ERR), which was in keeping with Biological Effects of Ionizing Radiation VII methods of risk transport (Eqq [A1 A3]) (6). All exceptions are described below. The following equations were used: Page 1 of 6

2 EAR EAR ( / ) ( / 60) exp( * M I s D a e ), (A1) [ EAR] EAR ERR ( / 60) ERR exp( * M D a e ), and (A2) s[err] ERR Radiation-induced cancer mortality rate, per organ = EAR 0.3 ERR 0.7. In Equations (A1) (A3), M and I correspond to current organ-, age-, and sex-specific background U.S. cancer mortality and incidence rates, respectively, derived from Surveillance Epidemiology and End Results data (17 19); s[ear] and s[err] correspond to organ- and sexspecific empirically derived coefficients that indicate excess absolute and relative risks, respectively, per sievert, at attained age of 60 years and at age at exposure older than 30 years. D corresponds to the organ-specific equivalent dose, derived from the effective dose level of a CT scan (described below). a corresponds to attained age; EAR and ERR are empirically derived exponential factors controlling response to attained age for the excess absolute and relative risk models, respectively. e* equals (e 30)/10, if e is less than 30, or 0 if e is greater than 30, where e is age at exposure. EAR and ERR are coefficients controlling response to age at exposure for the excess absolute and relative risk models, respectively, when e is less than 30 (6). The majority of empirically derived coefficients used to populate Equations (A1) (A3) in our model were elicited from the Biological Effects of Ionizing Radiation VII report (Table E1), with exceptions described below (6). Exponents for excess relative risk and excess absolute risk designated in Equation (A3) were applied to the majority of cancers in our model and indicate greater weighting of excess relative risk relative to excess absolute risk. This relative weighting was reversed in the case of lung cancer, per Biological Effects of Ionizing Radiation VII recommendations, because differential smoking behaviors across societies and generations were thought to reduce the validity of excess relative risk predominant assumptions of risk transport (6). Risk transport for central nervous system and thyroid cancers was accomplished by using only an excess relative risk model (6). For thyroid cancer, the excess relative risk equation also differed from Equation (A2), as described below. All estimates of radiation-induced cancer mortality rates were further divided by a dose and dose rate effectiveness factor of 1.5 to account for further assumed reductions in the setting of low exposure levels, per Biological Effects of Ionizing Radiation VII recommendations (6). We deviated from Biological Effects of Ionizing Radiation VII input estimates in a subset of organ systems (central nervous system, oral cavity, esophagus, rectum, pancreas, and kidney), for which we instead incorporated organ-specific parameters from Berrington de González and colleagues to estimate cancer risks (16). In contrast, the Biological Effects of Ionizing Radiation VII report treats these cancers collectively as other solid cancers (6). Radiation-induced Cancer Incidence The probability of developing radiation-induced cancers was modeled in an analogous way to that described previously, by computing organ-specific estimates of incidence rates as a function of excess absolute risk and excess relative risk and then introducing cumulative rates into the Markov model. Importantly, excess absolute risk and excess relative risk equations were Page 2 of 6 (A3)

3 modified such that they predicted excess risks of incidence rather than mortality, which meant that M / I was removed from Equation (A1) and that M was converted to I in Equation (A2) (6). Risk Models for Thyroid Cancer and Leukemia Risk models (eg, excess absolute risk and excess relative risk equations) for radiation-induced thyroid cancer and leukemia are distinct from those described previously. The Biological Effects of Ionizing Radiation VII model for thyroid cancer, which was used in our study, was based on analyses by investigators external to the Biological Effects of Ionizing Radiation VII committee and included pooled analyses of cohorts exposed to radiation in medical settings in addition to atomic bomb survivors (6). For leukemia, Biological Effects of Ionizing Radiation VII investigators determined that a linear-quadratic model (as opposed to a linear model) better fit data from the atomic bomb survivor cohort. As such, a linear-quadratic model was used to accomplish risk transport; relative weighting of excess absolute risk and excess relative risk was consistent with Equation (A3) (6). Importantly, current data indicate that chronic lymphocytic leukemia is rarely induced by radiation; chronic lymphocytic leukemia was thus excluded when calculating background incidence and mortality rates (6). Determination of Organ-specific Equivalent Doses from Effective Doses In our model, estimates of radiation-induced cancer mortality and incidence were computed at the organ level. Therefore, for every CT (eg, of abdomen and pelvis or chest), we needed to identify organ-specific equivalent doses (Eqq [A1, A2]) that corresponded to designated effective dose levels. This was accomplished by the use of commercially available dosimetry software (ImPACT CT, London, England) that enables a user to identify a set of organ-specific equivalent doses that correspond to a given effective dose for a designated anatomic region covered at a typical CT scan, by using simulation data derived from dosimetry studies of human phantoms and tissue-weighting coefficients derived from the International Commission on Radiation Protection (23,24). International Commission on Radiation Protection publication 60 tissue-weighting coefficients were used to maintain consistency in our methods; conversion factors used to estimate effective doses from institutional CT scans in our analysis (by using dose length product) also used International Commission on Radiation Protection publication 60 tissue-weighting coefficients (22,23). Markov Chain Monte Carlo Sensitivity Analysis We used a Markov Chain Monte Carlo method to estimate the composite uncertainty of the majority of parameters in our analysis, incorporating both cause-specific survival estimates from testicular cancer and the majority of radiation-related parameters (Table E1) (26). The Metropolis-Hastings search algorithm was used to identify one million unique parameter sets that accounted for the composite uncertainty of all model input parameters under consideration (26). For each imaging scenario, one million separate estimates were then generated for each outcome of interest (eg, life expectancy loss), thereby defining its associated uncertainty. In the Markov Chain Monte Carlo analysis, we incorporated distributions of all empirically derived parameters informing estimations of excess absolute risk and excess relative risk for which a quantitative estimate of uncertainty magnitude was available. We additionally incorporated uncertainty in the recommended weighting factors for excess absolute risk and Page 3 of 6

4 excess relative risk in all organ systems (Eqq [A3]). All incorporated radiation-related parameter estimates and their associated distributions are listed in Table E1. Subjective confidence intervals given in the Biological Effects of Ionizing Radiation VII report were used to inform uncertainty estimates for empirically derived model parameters (6). Uncertainty related to the propensity of developing or dying of solid cancers was reflected in organ-specific distributions, as well as in three distributions (corresponding to bladder, liver, and lung cancers in the excess absolute risk model) (6). Uncertainty related to the propensity of developing or dying of leukemia was reflected in distributions for multiple empirically derived parameters used in the linear-quadratic model for estimating leukemia risks (Table E1) (6). Page 4 of 6

5 Table E1. Radiation-related Parameter Estimates for Markov Chain Monte Carlo Analysis Parameter Distribution Male Parameter Estimate Excess relative risk model Bladder, Normal * Brain and central nervous system, Normal Colon, Normal Esophagus, Normal Kidney, Normal Leukemia, Normal * Leukemia, Normal * Leukemia, Normal Leukemia, Normal * Leukemia, Normal * Liver, Normal Lung, Normal Oral, Normal Pancreas, Normal Prostate, Normal * Rectum, Normal Stomach, Normal Thyroid, Normal * Excess absolute risk model Bladder, Normal * Bladder, n Normal Colon, Normal Esophagus, Normal Kidney, Normal Leukemia, Normal * Leukemia, Normal * Leukemia, Normal Leukemia, Normal * Liver, Normal Liver, n Normal Lung, Normal Lung, n Normal Oral, Normal Pancreas, Normal Prostate, Normal * Rectum, Normal Stomach, Normal Other Excess relative risk weighting factor, leukemia Excess relative risk weighting factor, lung cancer Excess relative risk weighting factor, solid cancer Triangular 0.7 (0.4 1) Triangular 0.3 (0 0.6) Triangular 0.7 (0.4 1) Page 5 of 6

6 Note. Unless otherwise indicated, parameter estimates are provided as means standard deviations. Point estimates for excess relative risk and excess absolute risk parameters and weighting factors are from the Biological Effects of Ionizing Radiation VII report (6), except excess relative risk and excess absolute risk parameters for the brain and central nervous system, oral cavity, esophagus, rectum, pancreas, and kidney, which are from Berrington de González et al (16). * Distributions truncated in the uncertainty analysis. Parameter estimates are provided as modes, with ranges in parentheses. In keeping with prior work by Berrington de González and colleagues (16), we elected to use normal distributions to represent the uncertainty in these parameters. Because the confidence intervals given in the Biological Effects of Ionizing Radiation VII report are asymmetric, we calculated an averaged standard deviation, from reported upper and lower confidence intervals, to generate corresponding symmetric distributions. Input parameter ranges were truncated in cases in which the calculated uncertainty range crossed zero. Importantly, to represent uncertainty surrounding organ-specific parameters derived from Berrington de González and colleagues (for organs that constituted the other solid cancers of the Biological Effects of Ionizing Radiation VII report), the confidence intervals given in the Biological Effects of Ionizing Radiation VII report for the other solid cancers category were proportionally applied to each individual value for these organ systems (6,16). A triangular distribution was chosen for the uncertainty surrounding weighting factors for the geometric mean of the excess absolute risk and excess relative risk models, which was in keeping with an approach used by Berrington de González and colleagues (51). Symmetric triangular distributions were generated for the weighting factors, with excess relative risk weights extending to zero (lung cancer) and one (other solid cancers and leukemia). Reference 51. Berrington de González A, Kim KP, Berg CD. Low-dose lung computed tomography screening before age 55: estimates of the mortality reduction required to outweigh the radiation-induced cancer risk. J Med Screen 2008;15(3): Page 6 of 6