Prognostics of systemic malignancy ICD-O topography and morphology types on brain metastases: an NCDB time-to-event cohort

Prognostics of systemic malignancy ICD-O topography and morphology types on brain metastases: an NCDB time-to-event cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostics of systemic malignancy ICD-O topography and morphology types on brain metastases: an NCDB time-to-event cohort Georgios Alexopoulos, Justin Zhang, Ioannis Karampelas, Mayur Patel, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1559460/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: The primary site and histology of systemic malignancy are known predictors of progression to brain metastases(BM). We investigated the combinational interactions of ICD-O primary topography and morphology types on the survival of BM after adjusting for relevant clinical and demographic prognostic factors. Methods: The cohort included all adult patients with BM at diagnosis of an invasive malignancy in the National Cancer Database(2010-2018). The sample consisted of 180,150 entries out of 14,279,749 cancer patients screened. A survival analysis of the topography- and histology- specific time to death was performed. Multivariate Cox regression revealed violations of the proportional hazard assumption for multiple covariates. Parametric models using a log-logistic distribution best described the population survival pattern. Results: The primary topography “prostate” and morphology “choriocarcinoma” provided the strongest survival benefit among ICD-O types, while BM from prostate demonstrated a 14-month median overall increase in survival probability. Favorable prognostics were BM from breast, bone/joints, and testis; also, the morphologies of carcinoid tumor, mature B-cell lymphoma, and papillary adenocarcinoma. Poor prognostics were BM from gastrointestinal(liver, biliary tree, pancreas, gallbladder) and gynecologic malignancies. All morphologies of spindle cell carcinoma, hemangiosarcoma, undifferentiated carcinoma, Ewing sarcoma, pseudosarcomatous carcinoma, renal cell carcinoma/sarcomatoid, signet ring cell carcinoma, spindle cell sarcoma, and squamous cell carcinoma/spindle cell were associated with poor survival. Conclusions: This is the largest cohort providing an unbiased estimate of the adjusted ICD-O topography and morphology effect sizes. The results can be summarized as a booklet for prognostic classification of disease in patients with BM secondary to systemic malignancy. brain metastases cerebral metastases brain metastasis metastatic disease topography morphology systemic malignancy Figures Figure 1 Figure 2 Figure 3 Figure 4 Importance of the study This study is the largest cohort of adult patients with synchronous brain metastases secondary to an invasive malignancy, as these reported in the National Cancer Database (NCDB). We investigate the combinational interactions between primary topography and morphology types, based on the International Classification for Diseases in Oncology. The results can be summarized as a booklet for prognostic classification of brain metastatic disease, and the study can become a valuable and updated companion to the graded prognostic assessment tool for clinical trial design. Introduction Brain metastases (BM) are the most frequent type of CNS tumor in adults. 1-3 Population-based studies report the incidence of metastatic brain cancer ranging from 8.3 to 14.3 per 100 000. 2-4 Other authors support that up to one-fifth of adult cancer tumors will eventually metastasize to the brain. 3,6,7 The exact incidence of BM remains unknown, while the reported rates in the literature are estimates at best. 1-7 Despite being a major source of morbidity and mortality, large-scale cohorts examining the prognostics of BMs are lacking. 1-6 Most of the survival data that do exist regarding patients with BM are based on studies decades old having several methodological limitations. 3,5,6 These studies are scarce, with only four population-based reports on BMs having been published recently. 2-6 Better understanding of the epidemiology and prognostics of BM will help identify individuals who are at greatest risk and guide clinicians in selecting patients who are most likely to benefit from surveillance and prophylaxis. 1-6 The natural history of progression to BM varies according to site (topography) and histology (morphology) of the systemic malignancy. 6-11 Lung cancer, breast cancer, melanoma and colorectal cancer are the most frequent to develop BM, and account for 67%-80% of all BM. 2,5,7,10 A tumor topography-related study in the Detroit metropolitan area from 1973 to 2001 reported that the incidence of BM was highest for lung (19.9%), followed by melanoma (6.9%), renal (6.5%), and breast (5.1%) cancers. 12 The overall prognosis of BM depends on the primary topography, histology, clinical, and treatment factors. 8,10-13 Despite these key reports, no large cohorts have established a time-to-event analysis implementing the combined effect of primary malignancy topography and morphology types on patient prognosis. In our study, we utilize data from the National Cancer Database of the American Cancer Society, 14 one of the largest hospital-based registries worldwide to identify the prognostics of various systemic malignancy topography and morphology types on BM, based upon the revised guidelines for International Classification of Diseases for Oncology(‎ICD-O)‎. 9 Through a comprehensive survival analysis workflow, our study is the first to report the combinational interactions of ICD-O primary topography and morphology types on the survival of patients with BM after adjusting for multiple relevant clinical and demographic factors. Methodology Data and study population Data were extracted from the National Cancer Database (NCDB). The NCDB is a joint program of the Commission on Cancer and the American Cancer Society including nationwide data from more than 1,500 Commission-accredited cancer facilities in the United States and Puerto Rico. 14 The entire NCDB adult registry [ages: 18-90+] from year 2010 to 2018 was filtered by “CS_METS_DX_BRAIN” == ‘YES’ (Item #: 2852; 2010-2015) OR “METS_AT_DX_BRAIN” == ‘YES’ (Item #: 1113; 2016-2018). All patients with BM at diagnosis of an invasive malignancy originating outside the CNS were included. Patients with non-invasive neoplasms were not included in this study. To control for Type S (sign) and Type M (magnitude) errors, 15 we retroactively performed a design analysis and found ICD-O types with small-sample brain metastases (N < 42) resulting in misleading statistically significant estimates. After removing the noisy small-sample sites of origin (Items #: 2852 OR 1113 == ‘YES’ < 42 patients with BM per topography), we identified a total of 180,325 subjects with BM out of 14,279,749 cancer patients screened (Table1). Based on the ICD-O-3 topographies, 9 175 patients with BM from extra-nodal NHLs and reported codes C71.0 to C72.9 were further excluded, given CNS was identified as the site of origin. The final sample consisted of 180,150 unique observations of cancer patients with BM and 91 variables were extracted from each record including the primary cancer type or topography, tumor histology or morphology, and the detailed anatomic site of origin (supplement). No duplicate patient ID entries were identified. All topography and morphology codes were reported according to ICD-O-3 (first revision). 9 Survival analysis The cohort included all cancer patients with BM at diagnosis as reported in the NCDB between the years 2010 and 2018. The target events for this study were the origin site-specific time to death from the time of diagnosis, or death attributable to primary topography, and the histology-specific time to death, or death attributable to morphology. The time origin was set as the point at which a subject was diagnosed with BM, and the time scale was the patient survival in months as reported in the NCDB. We had no reason to suspect informative censoring in a large multicenter database such as the NCDB. The events constituted independent random samples and the subpopulation was screened for duplicate patient entries. Non-parametric analysis was initially utilized to generate unbiased descriptive estimates, in conjunction with semi-parametric or parametric tests whenever necessary. Rank-based tests, such as the log-rank test, were used to statistically test the difference between the Kaplan-Meier survival curves. The semi-parametric Cox Proportional model was used for univariate and multiple regression analysis to estimate the hazard ratios. The proportional hazards (PH) assumption necessitates a constant relationship between the outcome and the covariates over time, and therefore, it is vital for interpretation of the Cox regression. The PH assumption for each predictor in the Cox models was tested calculating the scaled Schoenfeld residuals over time for factors, and the Martingale residuals for continuous variables. Parametric survival models, or accelerated failure time models (AFT), are alternatives to Cox regression, and one of the few available substitutes when the PH assumption is frankly violated. 16 Parametric survival analysis in our study included the exponential, Weibull, Gompertz, gamma, generalized gamma, lognormal and log-logistic distributions to identify the best survival population pattern to fit our data. Feature selection was performed using stepwise AIC backward regression by starting from a maximal model including all candidate predictor variables in the study. We used AIC and likelihood ratio tests to assess for relative model goodness of fit followed by the log(-log(S(t))) plots to check for model validity and evaluate the pattern of survival estimates against time. Here, we report the multiple regression analysis results of the best parametric model in conjunction with those extracted from Cox regression. Software All analyses were implemented using the R statistical software, version 4.1.2. Non-parametric and semiparametric survival approaches were completed using the “survival” and “survminer” packages. Feature selection was performed using “stepAIC” in MASS. Parametric distribution model fit was performed using the “flexsurv” package, while Kaplan-Meier estimates and the respective effect sizes from parametric bootstrap simulation were generated using the “survParamSim” implementation in R. Results 1) Univariate statistics 1.1 Nonparametric survival analysis The median topography-specific survival time in cancer patients with BM, or the time when the survival probability, S(t), decreased by 50%, was 17.9 months in tumors originating from the testis (95%CI[15.2,26.9];p < 0.0001). Patients with BM from nodal NHLs had a median survival of 13.6 months (95%CI[10.6,20.3];p < 0.0001), prostate metastases reached a S(t) of 15.8 months (95%CI[12.7,18.3];p < 0.0001) while patients with BM originating from breast had a S(t) of 10.7 months (95%CI[10.2,11.5];p < 0.0001), as shown in Figure 1. The topographies with the lowest median survival times were lung(other type), S(t) = 1.8 months (95%CI[1.74,1.84];p < 0.0001), pancreas, S(t) = 2.3 months (95%CI[2.20,2.60];p < 0.0001), urinary tracts, S(t) = 2.4 months (95%CI[1.87,3.30];p < 0.0001) and liver, S(t) = 2.6 months (95%CI[2.10,3.06];p < 0.0001). The Kaplan-Meier estimates of systemic malignancy topography on patient survival are demonstrated in Figures 1 and 2. When comparing the most common origin sites of BM, as previously reported in the literature, 1-6 there is a continuous survival advantage among patients with prostate cancer by a 14-month median overall increase in the survival probability. The median morphology-specific survival time was highest, or 18.4 months (95%CI[13.2,25.4]; p < 0.0001), in patients with infiltrating duct and lobular carcinoma. BM originating from acinar cell carcinomas had a median survival of 16.3 months (95%CI[12.6,18.6];p < 0.0001), while patients with malignant struma ovarii metastatic to the brain achieved an S(t) of 15.2 months (95%CI[13.7,19.3];p < 0.0001). BM originating from malignant neoplasms (ICD-O-3, #8000/3) had the lowest median survival of 1.3 months (95%CI[1.25,1.38];p < 0.0001), followed by spindle cell carcinomas not otherwise specified, S(t) = 2.4 months (95%CI[2.10,3.61];p < 0.0001) and hepatocellular carcinomas, S(t) = 2.7 months (95%CI[2.14,3.19];p < 0.0001). 1.2 Cox regression analysis The regression beta coefficients along with the hazard ratios (HR) and variable significance based on the topography and morphology of systemic disease were calculated for the variables of interest. Each predictor was assessed through separate Cox regression analysis followed by stratified Cox. The PH assumption was frankly violated for multiple covariates in the NCDB population, and proportionality was unable to be achieved after multiple stratification attempts (supplement). All the following primary topography types: pancreas, liver, biliary, urinary tracts, lung(other) were associated with poorer survival in patients with BM (Figure3). Tumors originating from testis, nodal NHL, extra-nodal NHL, and prostate were associated with improved survival. Furthermore, BM originating from extra-nodal NHLs reduced the hazard factor by 32% (HR= 0.68, 95%CI[0.52,0.88];p < 0.0001), followed by BM from testis with a HR decrease by 31% (HR= 0.69, 95%CI[0.59,0.81];p < 0.0001) when compared with metastases from prostate cancer (Figure3). In the univariate Cox regression, choriocarcinomas showed the best overall survival benefit among all morphology groups. Spindle cell carcinomas not otherwise specified (HR= 9.21, 95%CI[0.39,0.75]; p < 0.0001), hepatocellular carcinomas (HR= 8.21, 95%CI[4.72,14.28]; p < 0.0001), and malignant neoplasms (ICD-O-3 code #8000/3) were poor morphology prognostics in cancer patients with BM (supplement). 2) Multiple regression analysis 2.1 Feature selection Feature selection started from a full, or saturated, survival model including all 91 variables in the study (supplement). The optimal regression model was the one that minimized the AIC using stepwise backward elimination. The best model to describe the data was the one featuring the seventeen covariates demonstrated in Table2. 2.2 Semiparametric vs parametric survival analysis We fit a Cox model using all the significant covariates from feature selection. The HRs for each respective covariate can be seen in the supplement. The Schoenfeld residuals test was significant for multiple covariates in the model. The non-proportionality was further supported by graphical diagnostics given the log(- log(S(t))) plots did not demonstrate any parallelism (supplement). We were unable to correct for nonproportionality in the Cox model after multiple stratification attempts. We concluded that the estimates derived from utilizing Cox regression in the study should not be generalized, as semiparametric regression led to incorrect inferences. AFT models are especially important under such circumstances, given their parametric distribution for the survival times AFT models can make statistical inference accurate and lead to a proper model fitting. 16 2.3 Parametric model fit and results Relative to other parametric distribution results, the log-logistic distribution achieved the lowest AIC and likelihood ratio tests indicating a more parsimonious model able to better describe the NCDB population survival pattern (Figure4). The log-logistic distribution has a non-monotonic arc-shaped decreasing hazard rate. The absolute parametric model goodness of fit for validity was assessed through Q-Q graphical plots, which demonstrated linearity in a function of time for the loglogistic model. Next, we fit a loglogistic AFT model using all the significant variables from feature selection. 2.3.1 Topography and morphology We identified the topography “prostate” as the best overall prognostic among sites of origin in patients with BM (Table2). The median life expectancy for metastatic liver cancer was 10.1 times less (PO= 10.1, 95%CI[6.14,16.5];p < 0.0001) that of BM from prostate. BM from the biliary tree (PO= 8.14, 95%CI[5.37,12.3];p < 0.0001), pancreas (PO= 7.52, 95%CI[6.12,9.24];p < 0.0001), and gallbladder (PO= 7.17, 95%CI[4.67,11.0];p < 0.0001) were associated with poor survival. Similarly, ovarian (PO= 8.48,95%CI[6.09,11.8];p< 0.0001), uterine (PO= 8.49, 95%CI[6.61,10.9];p < 0.0001), and cervical (PO= 8.68, 95%CI[6.65,11.3];p < 0.0001) cancers were poor prognostics. In contrast, patients with BM originating from breast (PO= 3.44, 95%CI[2.81,4.22]; p < 0.0001), bone/joints (PO= 3.22, 95%CI[1.63,6.34];p < 0.0001), and testis (PO= 3.27, 95%CI[1.65,6.45];p < 0.0001) had an improved overall survival second only to that of prostate cancer. The histology “choriocarcinoma” has been reported as a potential favorable prognostic in BM, 17 therefore it was utilized as the morphology reference group in the model. Similarly, the histopathology choriocarcinoma was among the best overall morphology prognostics along with mature B-cell lymphomas (Table2). Other good prognostics were carcinoid tumor (PO= 2.72, 95%CI[1.11,6.68];p < 0.0001) and papillary adenocarcinoma (PO= 4.12, 95%CI[1.77,9.58];p < 0.0001). The combined morphology category cystic mucinous and serous carcinomas (ICD-O-3 codes #844-849) was associated with increased survival. In contrast, the median life expectancy for metastatic spindle cell carcinoma was 19.4 times less (PO= 19.4, 95%CI[7.77,48.4];p < 0.0001) that of choriocarcinoma. Hemangiosarcoma was also among the worse prognostics (PO= 18.6, 95%CI[7.3,47.2];p < 0.0001). The remaining morphology groups associated with poor survival were carcinoma undifferentiated (PO= 14.6, 95%CI[5.88,36.4];p < 0.0001), Ewing sarcoma (PO= 14.0, 95%CI[4.55,43.3];p < 0.0001), malignant neoplasm (PO= 15.9, 95%CI[6.91,36.7];p < 0.0001), pseudosarcomatous carcinoma (PO= 13.8, 95%CI[5.92,32.2];p < 0.0001), renal cell carcinoma/sarcomatoid (PO= 14.0, 95%CI[5.81,33.9];p < 0.0001), signet ring cell carcinoma (PO= 12.2, 95%CI[5.22,28.6];p < 0.0001), spindle cell sarcoma (PO= 14.5, 95%CI[5.34,39.2];p < 0.0001) and squamous cell carcinoma from spindle cells (PO= 13.5, 95%CI[4.97,36.5];p < 0.0001). Tumors in the combined morphology category osseous and chondromatous neoplasms (ICD-O-3 codes #918-924) were poor prognostics (Table2). 2.3.2 Demographics and patient specific factors The median survival for males was 1.24 times less that of females when adjusting for other covariates in the model (PO= 1.24, 95%CI[1.22,1.27];p < 0.0001). Increasing patient age, White race, and American Indians or Eskimos were bad prognostics (Table2). Asian race demonstrated a protective effect as the survival was accelerated by a factor of 1.3 (AF= 1.3, 95%CI[1.25,1.36];p < 0.0001) when compared to Black patients. Increasing Charlson-Deyo comorbidity score (CDScore) was associated with poor survival, while patients with a CDscore of 3 had the worse overall prognosis (PO= 1.52, 95%CI[1.45,1.59];p < 0.0001) among other CDscore groups. The median patient income and level of education did not achieve significance in the model. In patients with coexisting liver metastatic disease the median life expectancy was decreased by half (PO= 1.99, 95%CI[1.95,2.04];p < 0.0001). 2.3.3 Type of treatment Patients with BM who underwent surgery of the primary site with no residual tumor margins had 2.07 times increased overall survival (AF= 2.07, 95%CI[1.72,2.49];p < 0.0001). Failure of administering chemotherapy, despite being part of first course treatment, decreased the median survival by 4.31 (PO= 4.31, 95%CI[4.13,4.49];p < 0.0001). Similarly, no administration of radiotherapy (PO= 1.59, 95%CI[1.5,1.68];p < 0.0001), immunotherapy (PO= 2.01, 95%CI[1.66,2.44]; p < 0.0001) and hormone therapy (PO= 2.53, 95%CI[2.34,2.74];p < 0.0001) were all associated with shorter survival times. Discussion BM continue to foreshadow a poor prognosis for all cancer patients. 1-6 In 80% of the cases, BM are discovered in a metachronous fashion, but less frequently, BM can be diagnosed at the same time as the systemic malignancy (synchronous diagnosis). 5-8 The sources of BM(in descending order) are cancers of the lung, breast, skin, kidney, and gastrointestinal (GI) tract. 2,5,7,10 In the NCDB, all lung cancer morphology types demonstrated an increased frequency for synchronous BM with an approximately 9% of small cell lung cancer (SCLC) patients presenting with BM at the diagnosis of systemic disease (Table1). Previous studies have also reported 10% of SCLC patients overall presenting with synchronous BM. 1-4,21 Furthermore, GI tract malignancies in the study demonstrated an increased frequency of synchronous BM, especially patients with primary esophageal cancer (1.1%). The prognosis of BM varies depending on the primary topography, morphology, treatment status, and key patient factors. 1-8 The graded and upgraded prognostic assessments (GPA) remain some of the most valuable prognostic tools for common histologic types of BM. 13,18,19 The Radiation Therapy Oncology Group had previously developed a prognostic classification system for BM patients based on recursive partitioning analysis of performance status, age, and systemic tumor activity. 20 Large-scale studies reporting the combinational interactions of various prognostics on the survival of BM patients are still lacking. 1-8 Cagney et al utilized population-based data to identify 26,430 patients with synchronous BM and analyzed the incidences of various topographies; the authors reported BM secondary to prostate and breast cancer as having the longest median survival times. 10 We identified prostate cancer and breast cancer providing the strongest survival benefit among other topography types, while further reporting BM originating from bone/joints and testicular cancer as favorable prognostics. Gynecologic and GI malignancies with synchronous BM, especially those originating from liver, pancreas, uterus, cervix, and the ovaries demonstrated the poorest survival in our study. A recent systematic review reported that BM from cervical cancer could reduce longevity independent of overall tumor burden. 22 Our cohort is the first study to calculate the adjusted effect sizes between the various ICD-O topography types and further provide evidence to support such a statement (Table2). Scant single institution reports have identified patients with BM originating from choriocarcinoma and carcinoid tumors as potential long-term survivors. 17,23,24 Here, we showed how the histology choriocarcinoma was the most influential among other morphology types, followed by carcinoid tumor and mature B-cell lymphomas (Table2). Our study is also the first in the literature to identify various spindle cell-derived malignancies as poor prognostics, such as, spindle cell carcinoma, spindle cell sarcoma and squamous cell carcinoma from spindle cells. Choi et al reported the sarcomatoid histology component in BM from renal cell carcinomas(RCC) as a bad prognostic, 25 we further expand the authors’ statement to the sarcomatoid component in BM from RCC is a poor predictor of survival among all ICD-O morphologies. BM from GI signet ring cell carcinomas are extremely rare; 26 here we first identified the morphology as a bad prognostic. Pediatric BM from Ewing sarcoma carry a grave prognosis, 27 but adult BM from Ewing sarcoma also demonstrated a poor survival in our study. Favorable demographic factors for enhanced BM survival included female sex, Asian race and lower CDscore (Table2). Several efficacious therapeutic options were associated with improved survival in our cohort including surgery of the primary site without residual tumor margins, and, whenever part of first course treatment, the administration of radiotherapy, chemotherapy, immunotherapy, and hormone therapy; our findings here are in concordance with previous literature. 1-8 The accurate and generalizable estimation of effect sizes of the various influential survival predictors in cancer patients with BM is important for clinical trial design. This NCDB study is the largest published cohort, while our multiple regression model also provides an unbiased estimate of the various ICD-O topography and morphology effect sizes by simultaneously adjusting for multiple other known predictors. We provide an important updated companion to the GPA tool which would allow clinicians to estimate survival, individualize treatment, and stratify clinical trials in patients with BM based on individual ICD-O topography and morphology types. Furthermore, our study would help organize important clinical information and risk factors, leading to better identification, surveillance, improved patient counseling, more rigorous prognostic classification, and prophylactic treatment of cancer patients at greatest risk for BM. Limitations The primary limitations of this study are its integral data quality and the retrospective design. Although all essential factors were extracted from the NCDB to mitigate the risk of confounding, the possibility of influence from unmeasured confounders cannot be excluded. Real-world data are highly complex and an incomplete reflection of reality. There is always a chance for introduction of unpredictable outliers even with basic structural data collection. In concordance with previous literature, all survival predictors were included for feature selection except for the Karnofsky Performance Scale, as no such performance status variable has been reported in the NCDB. Nevertheless, the patient performance status is indirectly reflected by the administration of radiotherapy and systemic therapy in the database, covariates for which the model was appropriately adjusted. The NCDB is a hospital-based registry, therefore the defined populations and hospitals are subject to (and limited by) the regional referral patterns, regional access to health care and cancer treatment, and the inherent sampling biases of the pathology of that region. In addition, no two regions have equivalent treatment expertise. Slight variations in clinical aggressiveness in obtaining diagnostic imaging and/or surgery or even the frequency of biopsies potentially affect the reported incidences of BM. Randomized controlled trials would be ideal; however, it is neither practical nor feasible to establish a cohort on this scale. In addition, it is ethically unjustifiable to randomize newly diagnosed BM patients to a no-treatment placebo arm to assess for covariate significance. Declarations Acknowledgments We thank the National Cancer Database which is a joint project of the American Cancer Society and the Commission on Cancer of the American College of Surgeons for providing the data used in this study. The NCDB, established in 1989, is a nationwide, facility-based, comprehensive clinical surveillance resource oncology data set that currently captures 72% of all newly diagnosed malignancies in the US annually. The American College of Surgeons and the Commission on Cancer are not responsible for the analytic or statistical methodology employed in the study. We are indebted to many clinicians for their contributions to this registry, and to the patients for participation in research. Funding Dr. Philippe Mercier was supported by a Saint Louis University College of Medicine Clinical Research Scholarship. Statistical assistance was provided by Noor Al-Hammadi. Author contributions Conception and design: Georgios Alexopoulos Provision of study materials or patients: Philippe Mercier, Georgios Alexopoulos Collection and assembly of data: Georgios Alexopoulos, Justin Zhang Data analysis and interpretation: Georgios Alexopoulos Manuscript writing: Georgios Alexopoulos Assisted with manuscript writing: Justin Zhang, Mayur Patel, Ioannis Karampelas Revised the final manuscript: Georgios Alexopoulos, Ioannis Karampelas, Philippe Mercier Final approval of manuscript: All authors Accountable for all aspects of the work: All authors Data Availability The datasets generated and analysed during the current study are available in the National Cancer Database ( NCDB) repository, https://ncdbapp.facs.org/puf/ . Ethics approval This is an observational study. The XYZ Research Ethics Committee has confirmed that no ethical approval is required. Conflict of Interest Statement: None declared. The authors declare no potential conflicts of interest related to this study. References Fox BD, Cheung VJ, Patel AJ, Suki D, Rao G. Epidemiology of metastatic brain tumors. Neurosurg Clin N Am. 2011 Jan;22(1):1-6, v. doi: 10.1016/j.nec.2010.08.007. PMID: 21109143. Nayak L, Lee EQ, Wen PY. Epidemiology of brain metastases. Curr Oncol Rep. 2012 Feb;14(1):48-54. doi: 10.1007/s11912-011-0203-y. PMID: 22012633. Sacks P, Rahman M. Epidemiology of Brain Metastases. Neurosurg Clin N Am. 2020 Oct;31(4):481-488. doi: 10.1016/j.nec.2020.06.001. PMID: 32921345. Ostrom QT, Wright CH, Barnholtz-Sloan JS. Brain metastases: epidemiology. Handb Clin Neurol. 2018;149:27-42. doi: 10.1016/B978-0-12-811161-1.00002-5. PMID: 29307358. Lamba N, Wen PY, Aizer AA. Epidemiology of brain metastases and leptomeningeal disease. Neuro Oncol. 2021 Sep 1;23(9):1447-1456. doi: 10.1093/neuonc/noab101. PMID: 33908612; PMCID: PMC8408881. Lin X, DeAngelis LM. Treatment of Brain Metastases. J Clin Oncol. 2015 Oct 20;33(30):3475-84. doi: 10.1200/JCO.2015.60.9503. Epub 2015 Aug 17. PMID: 26282648; PMCID: PMC5087313. Lowery FJ, Yu D. Brain metastasis: Unique challenges and open opportunities. Biochim Biophys Acta Rev Cancer . 2017;1867(1):49-57. doi:10.1016/j.bbcan.2016.12.001 Valiente M, Ahluwalia MS, Boire A, et al. The Evolving Landscape of Brain Metastasis. Trends Cancer . 2018;4(3):176-196. doi:10.1016/j.trecan.2018.01.003 World Health Organization. (‎2013)‎. International classification of diseases for oncology (‎ICD-O)‎, 3rd ed., 1st revision. World Health Organization. Cagney DN, Martin AM, Catalano PJ, Redig AJ, Lin NU, Lee EQ, Wen PY, Dunn IF, Bi WL, Weiss SE, Haas-Kogan DA, Alexander BM, Aizer AA. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro Oncol. 2017 Oct 19;19(11):1511-1521. doi: 10.1093/neuonc/nox077. PMID: 28444227; PMCID: PMC5737512. Gállego Pérez-Larraya J, Hildebrand J. Brain metastases. Handb Clin Neurol. 2014;121:1143-57. doi: 10.1016/B978-0-7020-4088-7.00077-8. PMID: 24365409. Barnholtz-Sloan JS, Sloan AE, Davis FG, Vigneau FD, Lai P, Sawaya RE. Incidence proportions of brain metastases in patients diagnosed (1973 to 2001) in the Metropolitan Detroit Cancer Surveillance System. J Clin Oncol. 2004 Jul 15;22(14):2865-72. doi: 10.1200/JCO.2004.12.149. PMID: 15254054 Sperduto PW, Kased N, Roberge D, Xu Z, Shanley R, Luo X, Sneed PK, Chao ST, Weil RJ, Suh J, Bhatt A, Jensen AW, Brown PD, Shih HA, Kirkpatrick J, Gaspar LE, Fiveash JB, Chiang V, Knisely JP, Sperduto CM, Lin N, Mehta M. Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin Oncol. 2012 Feb 1;30(4):419-25. doi: 10.1200/JCO.2011.38.0527. Epub 2011 Dec 27. PMID: 22203767; PMCID: PMC3269967. American College of Surgeons. National Cancer Database. https://www-facs-org.ezp.slu.edu/quality-programs/cancer/ncdb . Accessed March 08, 2022. Gelman A, Carlin J. Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspect Psychol Sci. 2014 Nov;9(6):641-51. doi: 10.1177/1745691614551642. PMID: 26186114. Alexopoulos G, Zhang J, Karampelas I, Patel M, Kemp J, Coppens J, Mattei TA, Mercier P. Long-term time series forecasting and updates on survival analysis of glioblastoma multiforme, a 1975-2018 population-based study. Neuroepidemiology. 2022 Feb 16. doi: 10.1159/000522611. Epub ahead of print. PMID: 35172317. Athanassiou A, Begent RH, Newlands ES, Parker D, Rustin GJ, Bagshawe KD. Central nervous system metastases of choriocarcinoma. 23 years' experience at Charing Cross Hospital. Cancer. 1983 Nov 1;52(9):1728-35. doi: 10.1002/1097-0142(19831101)52:93.0.co;2-u. PMID: 6684500. Sperduto PW, Chao ST, Sneed PK, Luo X, Suh J, Roberge D, Bhatt A, Jensen AW, Brown PD, Shih H, Kirkpatrick J, Schwer A, Gaspar LE, Fiveash JB, Chiang V, Knisely J, Sperduto CM, Mehta M. Diagnosis-specific prognostic factors, indexes, and treatment outcomes for patients with newly diagnosed brain metastases: a multi-institutional analysis of 4,259 patients. Int J Radiat Oncol Biol Phys. 2010 Jul 1;77(3):655-61. doi: 10.1016/j.ijrobp.2009.08.025. Epub 2009 Nov 26. PMID: 19942357. Sperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, Nesbit E, Kruser TJ, Chan J, Braunstein S, Lee J, Kirkpatrick JP, Breen W, Brown PD, Shi D, Shih HA, Soliman H, Sahgal A, Shanley R, Sperduto WA, Lou E, Everett A, Boggs DH, Masucci L, Roberge D, Remick J, Plichta K, Buatti JM, Jain S, Gaspar LE, Wu CC, Wang TJC, Bryant J, Chuong M, An Y, Chiang V, Nakano T, Aoyama H, Mehta MP. Survival in Patients With Brain Metastases: Summary Report on the Updated Diagnosis-Specific Graded Prognostic Assessment and Definition of the Eligibility Quotient. J Clin Oncol. 2020 Nov 10;38(32):3773-3784. doi: 10.1200/JCO.20.01255. Epub 2020 Sep 15. PMID: 32931399; PMCID: PMC7655019. Gaspar L, Scott C, Rotman M, Asbell S, Phillips T, Wasserman T, McKenna WG, Byhardt R. Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys. 1997 Mar 1;37(4):745-51. doi: 10.1016/s0360-3016(96)00619-0. PMID: 9128946. Waqar SN, Samson PP, Robinson CG, Bradley J, Devarakonda S, Du L, Govindan R, Gao F, Puri V, Morgensztern D. Non-small-cell Lung Cancer With Brain Metastasis at Presentation. Clin Lung Cancer. 2018 Jul;19(4):e373-e379. doi: 10.1016/j.cllc.2018.01.007. Epub 2018 Mar 9. PMID: 29526531; PMCID: PMC6990432 Takayanagi A, Florence TJ, Hariri OR, Armstrong A, Yazdian P, Sumida A, Quadri SA, Cohen J, Tehrani OS. Brain metastases from cervical cancer reduce longevity independent of overall tumor burden. Surg Neurol Int. 2019 Sep 13;10:176. doi: 10.25259/SNI_37_2019. PMID: 31583173; PMCID: PMC6763668. Spears WT, Morphis JG 2nd, Lester SG, Williams SD, Einhorn LH. Brain metastases and testicular tumors: long-term survival. Int J Radiat Oncol Biol Phys. 1992;22(1):17-22. doi: 10.1016/0360-3016(92)90977-p. PMID: 1370066. Hlatky R, Suki D, Sawaya R. Carcinoid metastasis to the brain. Cancer. 2004 Dec 1;101(11):2605-13. doi: 10.1002/cncr.20659. PMID: 15495181. Choi SY, Yoo S, You D, Jeong IG, Song C, Hong B, Hong JH, Ahn H, Kim CS. Prognostic Factors for Survival of Patients With Synchronous or Metachronous Brain Metastasis of Renal Cell Carcinoma. Clin Genitourin Cancer. 2017 Dec;15(6):717-723. doi: 10.1016/j.clgc.2017.05.010. Epub 2017 May 10. PMID: 28552571. Ali S, Khan MT, Idrisov EA, Maqsood A, Asad-Ur-Rahman F, Abusaada K. Signet Cell in the Brain: A Case Report of Leptomeningeal Carcinomatosis as the Presenting Feature of Gastric Signet Cell Cancer. Cureus. 2017 Mar 7;9(3):e1085. doi: 10.7759/cureus.1085. PMID: 28405535; PMCID: PMC5384845. Parasuraman S, Langston J, Rao BN, Poquette CA, Jenkins JJ, Merchant T, Cain A, Pratt CB, Pappo AS. Brain metastases in pediatric Ewing sarcoma and rhabdomyosarcoma: the St. Jude Children's Research Hospital experience. J Pediatr Hematol Oncol. 1999 Sep-Oct;21(5):370-7. doi: 10.1097/00043426-199909000-00007. PMID: 10524449. Abbreviations AF, acceleration factor; AFT, accelerated failure time; AIC, Akaike information criterion; BM, brain metastases; CDscore, Charlson-Deyo comorbidity score; GI, gastrointestinal; GPA, graded prognostic assessment; HR, hazard ratio; ICD-O, International Classification of Diseases for Oncology; NCDB, National Cancer Database; NHL, Non-Hodgkin lymphoma; PH, proportional hazards; PO, proportional odds; RCC, renal cell carcinoma; SCLC small cell lung cancer. Tables Table 1. Frequency Tables. Incidence of Cancer Patients in the National Cancer Database (NCDB) with Synchronous Brain Metastases stratified by ICD-O Primary Topography from year 2010 to 2018. ICD-O Primary Topography Total N Cases Brain Metastases at diagnosis (N,%) Anus 78,407 64 (0.08) Biliary 108,434 225(0.21) Bone/Joint 28,411 86 (0.30) Breast 3,208,696 7,077 (0.22) Cervix 148,113 317 (0.21) Colon 1,490,531 2,814 (0.19) Digestive tract, other 16,905 385 (2.28) Esophagus 181,586 2,027 (1.11) Female genitals/placenta 30,428 89 (0.29) Gallbladder 41,967 87 (0.21) Kidney 615,519 5,106 (0.83) Liver 233,700 506 (0.22) Lung, non-small cell 1,772,978 109,782 (6.19) Lung, other 157,582 11,554 (7.33) Lung, small cell 296,583 26,359 (8.89) Melanoma 704,041 6,902 (0.98) Nasal 27,600 150 (0.54) NHL, extra-nodal 207,804 175 (0.08) NHL, nodal 442,772 514 (0.12) Ovary 245,465 258 (0.11) Pancreas 459,547 1,178 (0.26) Skin, other 47,204 56 (0.12) Prostate 1,742,871 720 (0.04) Small Intestine 87,435 137 (0.16) Soft Tissues 119,330 513 (0.43) Stomach 237,704 955 (0.40) Testis 83,903 534 (0.64) Trachea/Larynx/Mediastinum 150,097 130 (0.08) Urinary Tracts 46,586 103 (0.22) Urinary Bladder 671,462 520 (0.08) Uterus 596,088 827 (0.14) Total Number of Patients 14,279,749 180,150(1.26) Table 2 . Parametric Survival Analysis. Accelerated Failure Time Model Using the Best Distribution (Log-logistic) to Describe the Survival Pattern in the Population, NCDB from 2010-2018 , N = 145,429 Predictor Variable Acceleration Factor † (95% CI) Proportional Odds ‡ (95% CI) p-value ICD-O topography Prostate * * * Anus 0.32 (0.22, 0.46) 5.71 (3.26, 9.99) <0.001 Biliary 0.25 (0.19, 0.33) 8.14 (5.37, 12.3) <0.001 Bone/joint 0.46 (0.29, 0.72) 3.22 (1.63, 6.34) <0.001 Breast 0.44 (0.39, 0.51) 3.44 (2.81, 4.22) <0.001 Cervix 0.24 (0.20, 0.29) 8.68 (6.65, 11.3) <0.001 Colon 0.32 (0.29, 0.37) 5.50 (4.55, 6.66) <0.001 Digestive, other 0.30 (0.25, 0.35) 6.27 (4.81, 8.16) <0.001 Esophagus 0.30 (0.26, 0.34) 6.20 (5.11, 7.53) <0.001 Female genitals/placenta 0.31 (0.22, 0.44) 5.91 (3.49, 10.0) <0.001 Gallbladder 0.27 (0.20, 0.36) 7.17 (4.67, 11.0) <0.001 Kidney 0.31 (0.27, 0.36) 5.91 (4.71, 7.42) <0.001 Liver 0.22 (0.16, 0.30) 10.1 (6.14, 16.5) <0.001 Lung, non-small cell 0.36 (0.32, 0.40) 4.77 (3.99, 5.69) <0.001 Lung, other type 0.31 (0.27, 0.35) 5.88 (4.83, 7.17) <0.001 Lung, small cell 0.40 (0.33, 0.48) 4.02 (2.99, 5.40) <0.001 Melanoma 0.29 (0.22, 0.38) 6.52 (4.28, 9.94) <0.001 Nasal 0.39 (0.31, 0.51) 4.06 (2.77, 5.96) <0.001 NHL Extra-nodal 0.14 (0.03, 0.58) 19.2 (2.25, 164) 0.006 NHL Nodal 0.09 (0.02, 0.39) 34.8 (4.08, 296) 0.002 Ovary 0.24 (0.19, 0.30) 8.48 (6.09, 11.8) <0.001 Pancreas 0.26 (0.23, 0.30) 7.52 (6.12, 9.24) <0.001 Skin, other 0.43 (0.29, 0.64) 3.62 (1.97, 6.65) <0.001 Small Intestine 0.31 (0.24, 0.39) 5.94 (4.12, 8.57) <0.001 Soft Tissues 0.34 (0.27, 0.43) 5.10 (3.61, 7.21) <0.001 Stomach 0.27 (0.24, 0.31) 7.20 (5.81, 8.93) <0.001 Testis 0.46 (0.29, 0.72) 3.27 (1.65, 6.45) <0.001 Trachea/Larynx/Mediastinum 0.38 (0.27, 0.53) 4.35 (2.58, 7.34) <0.001 Urinary Tracts 0.28 (0.21, 0.39) 6.58 (4.20, 10.3) <0.001 Urinary Bladder 0.29 (0.24, 0.36) 6.44 (4.77, 8.71) <0.001 Uterus 0.24 (0.21, 0.29) 8.49 (6.61, 10.9) <0.001 Facility Type Academic/Research Program 1.06 (1.01, 1.12) 0.91 (0.84, 0.99) 0.035 Community Cancer Program 0.90 (0.85, 0.95) 1.17 (1.07, 1.29) <0.001 Comprehensive Community Cancer Program 0.92 (0.87, 0.97) 1.14 (1.05, 1.24) 0.003 Integrated Cancer Program 0.92(0.87, 0.98) 1.13 (1.03, 1.23) 0.007 Age 0.99 (0.988, 0.989) 1.02 (1.02, 1.02) <0.001 Sex—Male 0.87 (0.855, 0.877) 1.24 (1.22, 1.27) <0.001 Race Black * * * American Indian or Eskimo 0.89 (0.80, 0.99) 1.18 (1.01, 1.39) 0.041 Asian 1.30 (1.25, 1.36) 0.67 (0.63, 0.72) <0.001 Other 1.22 (1.13, 1.32) 0.74 (0.66, 0.83) <0.001 Pacific Islander 1.16 (1.01, 1.34) 0.79 (0.64, 0.98) 0.036 Unknown 1.12 (1.04, 1.21) 0.84 (0.75, 0.94) 0.002 White 0.92 (0.90, 0.94) 1.13 (1.10, 1.16) <0.001 Median Income (2016) $30,000 to 34,999 0.96 (0.85, 1.10) 1.06 (0.87, 1.28) 0.580 $35,000 to 45,999 0.99 (0.87, 1.13) 1.02 (0.83, 1.24) 0.877 Above $46,000 1.08 (0.95, 1.23) 0.89 (0.73, 1.08) 0.227 Less $30,000 0.92 (0.81, 1.05) 1.13 (0.93, 1.38) 0.214 Urban or Rural (2013) Metropolitan 0.95 (0.91, 0.98) 1.09 (1.02, 1.15) 0.006 Rural 0.91 (0.86, 0.97) 1.15 (1.05, 1.25) 0.002 Urban 0.93 (0.89, 0.97) 1.12 (1.05, 1.19) <0.001 High school degree (2016) 10.9% to 17.6% 0.97 (0.84, 1.12) 1.05 (0.85, 1.31) 0.647 6.3% to 10.8% 0.95 (0.82, 1.10) 1.08 (0.87, 1.34) 0.497 Above 17.6% 1.03 (0.89, 1.19) 0.95 (0.77, 1.18) 0.662 Less 6.3% 0.97 (0.84, 1.11) 1.05 (0.85, 1.31) 0.640 Total Charlson-Deyo Score 0 * * * 1 0.87 (0.85, 0.88) 1.24 (1.22, 1.27) <0.001 2 0.80 (0.79, 0.82) 1.38 (1.34, 1.43) <0.001 3 0.76 (0.74, 0.78) 1.52 (1.45, 1.59) <0.001 Great Circle Distance (miles) 1 (1, 1) 1 (1, 1) <0.001 ICD-O morphology Choriocarcinoma, NOS * * * Acinar cell carcinoma 0.39 (0.22, 0.68) 4.23 (1.81, 9.88) <0.001 Acinar cell cystadenocarcinoma 0.15 (0.02, 1.00) 18.1 (0.99, 328) 0.050 Adenocarcinomas, other 0.27 (0.15, 0.46) 7.44 (3.24, 17.1) <0.001 Adenosquamous carcinoma 0.22 (0.13, 0.38) 9.95 (4.31, 23.0) <0.001 Adnexal and skin appendage neoplasms 0.19 (0.09, 0.42) 12.0 (3.74, 38.7) <0.001 Atypical carcinoid tumor 0.35 (0.19, 0.65) 4.83 (1.91, 12.2) <0.001 Basal cell carcinomas, other 0.11 (0.02, 0.75) 27.7 (1.53, 501) 0.024 Basaloid squamous cell carcinoma 0.24 (0.13, 0.43) 8.67 (3.50, 21.5) <0.001 Blood vessel tumors, other 0.45 (0.17, 1.17) 3.34 (0.78, 14.2) 0.103 Carcinoid tumor, NOS 0.52 (0.28, 0.93) 2.72 (1.11, 6.68) 0.029 Carcinoma, other 0.21 (0.12, 0.36) 10.9 (4.74, 25.1) <0.001 Carcinoma, undifferentiated, NOS 0.17 (0.09, 0.31) 14.6 (5.88, 36.4) <0.001 Carcinosarcoma, NOS 0.22 (0.12, 0.40) 9.82 (4.02, 24.0) <0.001 Cholangiocarcinoma 0.27 (0.15, 0.51) 7.17 (2.80, 18.4) <0.001 Clear cell adenocarcinoma, NOS 0.33 (0.19, 0.57) 5.46 (2.34, 12.7) <0.001 CNS embryonal tumor, NOS 0.10 (0.02, 0.42) 32.6 (3.71, 287) 0.002 Combined small cell carcinoma 0.21 (0.12, 0.38) 10.6 (4.39, 25.7) <0.001 Complex epithelial neoplasms 0.19 (0.09, 0.37) 12.3 (4.51, 33.8) <0.001 Complex mixed and stromal neoplasms 0.19 (0.10, 0.35) 12.7 (4.90, 33.0) <0.001 Cystic, mucinous, and serous carcinomas 0.53 (0.26, 1.07) 2.61 (0.90, 7.53) 0.076 Ductal and lobular carcinomas, other 0.24 (0.12, 0.45) 8.77 (3.33, 23.1) <0.001 Endometrioid adenocarcinoma, NOS 0.23 (0.13, 0.41) 9.21 (3.84, 22.1) <0.001 Ewing sarcoma 0.17 (0.08, 0.37) 14.0 (4.55, 43.3) <0.001 Fibroepithelial neoplasms 0.08 (0.02, 0.29) 42.7 (6.42, 284) <0.001 Fibromatous neoplasms 0.22 (0.11, 0.43) 10.2 (3.57, 28.9) <0.001 Germ cell neoplasms 0.07 (0.02, 0.20) 56.0 (11.8, 267) <0.001 Giant cell tumors 0.12 (0.02, 0.53) 24.7 (2.64, 231) 0.005 Granular cell tumors and alveolar soft part sarcomas 0.71 (0.32, 1.56) 1.69 (0.51, 5.57) 0.390 Hemangiosarcoma 0.14 (0.08, 0.27) 18.6 (7.30, 47.2) <0.001 Hepatocellular carcinoma, all types 0.26 (0.14, 0.50) 7.53 (2.86, 19.8) <0.001 Hepatoid adenocarcinoma 0.19 (0.09, 0.38) 11.7 (4.21, 32.8) <0.001 Infiltrating duct and lobular carcinoma 0.29 (0.16, 0.53) 6.42 (2.64, 15.6) <0.001 Infiltrating duct carcinoma, NOS 0.24 (0.13, 0.41) 8.93 (3.86, 20.7) <0.001 Infiltrating duct mixed with other types of carcinoma 0.31 (0.17, 0.58) 5.78 (2.26, 14.8) <0.001 Inflammatory carcinoma 0.19 (0.10, 0.34) 12.7 (5.10, 31.4) <0.001 Large cell carcinoma, NOS 0.21 (0.12, 0.36) 10.8 (4.66, 24.9) <0.001 Large cell neuroendocrine carcinoma 0.23 (0.13, 0.41) 8.96 (3.88, 20.7) <0.001 Leiomyosarcoma, NOS 0.27 (0.14, 0.50) 7.25 (2.87, 18.3) <0.001 Lepidic adenocarcinoma 0.32 (0.18, 0.57) 5.57 (2.35, 13.2) <0.001 Leukemias, other 2.70 (0.56, 13.0) 0.22 (0.02, 2.40) 0.216 Lipomatous sarcomas 0.25 (0.11, 0.53) 8.38 (2.62, 26.8) <0.001 Lobular carcinoma, NOS 0.25 (0.14, 0.44) 8.32 (3.52, 19.6) <0.001 Lymphoid leukemias 0.82 (0.17, 4.0) 1.35 (0.12, 14.8) 0.808 Malignant lymphoma, diffuse 1.41 (0.32, 6.15) 0.59 (0.06, 5.51) 0.646 Malignant lymphoma, large B-cell, diffuse, NOS 0.84 (0.22, 3.09) 1.31 (0.18, 9.44) 0.790 Malignant lymphoma, non-Hodgkin, NOS 1.33 (0.33, 5.31) 0.65 (0.08, 5.23) 0.683 Malignant melanoma, NOS 0.38 (0.21, 0.69) 4.33 (1.74, 10.8) 0.002 Malignant peripheral nerve sheath tumor 0.19 (0.09, 0.40) 12.7 (3.99, 40.4) <0.001 Malignant tumor, NOS 0.31 (0.15, 0.64) 5.82 (1.96, 17.3) 0.002 Mature B-cell lymphomas, other 2.10 (0.55, 8.07) 0.32 (0.04, 2.49) 0.279 Mature T- and NK-cell lymphomas 0.63 (0.16, 2.51) 2.03 (0.25, 16.7) 0.508 Melanoma, other 0.38 (0.20, 0.71) 4.38 (1.70, 11.3) 0.002 Miscellaneous tumors, other 0.47 (0.07, 3.15) 3.15 (0.18, 56.5) 0.436 Mixed cell adenocarcinoma 0.32 (0.17, 0.60) 5.56 (2.16, 14.4) <0.001 Mucin-producing adenocarcinoma 0.26 (0.15, 0.46) 7.53 (3.17, 17.9) <0.001 Mucinous adenocarcinoma 0.24 (0.14, 0.41) 8.75 (3.78, 20.3) <0.001 Mucoepidermoid carcinoma 0.26 (0.12, 0.59) 7.47 (2.19, 25.5) 0.001 Mullerian mixed tumor 0.23 (0.11, 0.48) 9.03 (3.08, 26.5) <0.001 Myomatous neoplasms, other 0.15 (0.07, 0.29) 17.8 (6.31, 50.0) <0.001 Myxomatous neoplasms 2.20 (0.41, 11.9) 0.30 (0.02, 3.91) 0.360 Neoplasm, malignant 0.16 (0.09, 0.28) 15.9 (6.91, 36.7) <0.001 Neuroendocrine carcinoma, NOS 0.24 (0.14, 0.43) 8.39 (3.64, 19.3) <0.001 Neuroepitheliomatous neoplasms 0.78 (0.30, 2.09) 1.45 (0.33, 6.44) 0.624 Nodular melanoma 0.25 (0.14, 0.47) 7.95 (3.11, 20.3) <0.001 Non-small cell carcinoma 0.21 (0.12, 0.36) 10.6 (4.60, 24.3) <0.001 Oat cell carcinoma 0.20 (0.11, 0.38) 11.1 (4.35, 28.1) <0.001 Osseous and chondromatous neoplasms 0.14 (0.07, 0.29) 19.5 (6.34, 59.7) <0.001 Papillary adenocarcinoma, NOS 0.39 (0.22, 0.68) 4.12 (1.77, 9.58) <0.001 Papillary carcinoma, NOS 0.25 (0.13, 0.47) 8.14 (3.10, 21.4) <0.001 Papillary transitional cell carcinoma 0.26 (0.14, 0.49) 7.53 (2.97, 19.1) <0.001 Paragangliomas and glomus tumors 3.56 (0.52, 24.3) 0.15 (0.01, 2.68) 0.195 Plasma cell tumors 1 (1, 1) 1 (1, 1) N/A Pleomorphic carcinoma 0.19 (0.10, 0.34) 12.7 (5.19, 30.9) <0.001 Pseudosarcomatous carcinoma 0.18 (0.10, 0.31) 13.8 (5.92, 32.2) <0.001 Renal cell carcinoma, NOS 0.27 (0.15, 0.47) 7.19 (3.08, 16.8) <0.001 Renal cell carcinoma, other 0.23 (0.10, 0.50) 9.26 (2.83, 30.3) <0.001 Renal cell carcinoma, sarcomatoid 0.17 (0.09, 0.31) 14.0 (5.81, 33.9) <0.001 Serous carcinoma, NOS 0.29 (0.16, 0.55) 6.34 (2.46, 16.3) <0.001 Signet ring cell carcinoma 0.19 (0.11, 0.34) 12.2 (5.22, 28.6) <0.001 Small cell carcinoma, intermediate cell 0.21 (0.10, 0.42) 10.8 (3.75, 31.0) <0.001 Small cell carcinoma, NOS 0.20 (0.11, 0.36) 11.1 (4.68, 26.5) <0.001 Soft tissue sarcomas, NOS 0.18 (0.10, 0.33) 12.8 (5.30, 31.1) <0.001 Solid carcinoma, NOS 0.30 (0.16, 0.55) 6.15 (2.47, 15.3) <0.001 Specialized gonadal neoplasms 0.07 (0.01, 0.49) 53.2 (2.90, 975) 0.007 Spindle cell carcinoma, NOS 0.14 (0.07, 0.26) 19.4 (7.77, 48.4) <0.001 Spindle cell melanoma, NOS 0.43 (0.21, 0.86) 3.61 (1.25, 10.4) 0.018 Spindle cell sarcoma 0.17 (0.09, 0.33) 14.5 (5.34, 39.2) <0.001 Squamous cell carcinoma, keratinizing, NOS 0.20 (0.12, 0.36) 10.7 (4.61, 25.0) <0.001 Squamous cell carcinoma, large cell, non-keratinizing, NOS 0.22 (0.13, 0.39) 9.72 (4.12, 23.0) <0.001 Squamous cell carcinoma, NOS 0.21 (0.12, 0.36) 10.6 (4.62, 24.4) <0.001 Squamous cell carcinoma, spindle cell 0.18 (0.09, 0.34) 13.5 (4.97, 36.5) <0.001 Squamous cell neoplasms, other 0.18 (0.09, 0.36) 13.1 (4.71, 36.3) <0.001 Struma ovarii, malignant 0.20 (0.10, 0.38) 11.6 (4.35, 30.8) <0.001 Synovial sarcomas 0.16 (0.08, 0.34) 15.5 (5.10, 47.3) <0.001 Thymic epithelial neoplasms 0.14 (0.02, 0.98) 18.5 (1.03, 335) 0.048 Transitional cell carcinomas, other 0.23 (0.13, 0.41) 9.09 (3.83, 21.6) <0.001 Trophoblastic neoplasms, other 0.53 (0.15, 1.87) 2.56 (0.39, 16.9) 0.329 Summary of Surgical Margins (surgery at primary site) Macroscopic residual tumor * * * Margins not evaluable 0.97 (0.79, 1.19) 1.04 (0.76, 1.41) 0.800 Microscopic residual tumor 1.31 (1.06, 1.62) 0.66 (0.48, 0.91) 0.011 No primary site surgery 0.84 (0.67, 1.0) 1.31 (0.99, 1.72) 0.053 No residual tumor 2.07 (1.72, 2.49) 0.33 (0.25, 0.44) <0.001 Residual tumor, NOS 1.13 (0.93, 1.38) 0.83 (0.61, 1.12) 0.218 Unknown 1.38 (1.14, 1.68) 0.61 (0.46, 0.82) 0.001 Summary of Chemotherapy Administered * * * Contraindicated 0.30 (0.29, 0.30) 6.30 (6.03, 6.59) <0.001 Not administered 0.38 (0.37, 0.39) 4.31 (4.13, 4.49) <0.001 Not administered, patient died 0.18 (0.18, 0.19) 12.6 (12.0, 13.2) <0.001 Not part of first course Tx 0.35 (0.35, 0.36) 4.84 (4.73, 4.95) <0.001 Unknown 0.63 (0.61, 0.66) 2.0 (1.88, 2.13) <0.001 Summary of Radiation Therapy Administered * * * Contraindicated 0.54 (0.51, 0.57) 2.52 (2.31, 2.76) <0.001 Not administered 0.74 (0.71, 0.76) 1.59 (1.50, 1.68) <0.001 Not administered, patient died 0.37 (0.32, 0.44) 4.45 (3.49, 5.68) <0.001 Not part of first course Tx 0.71 (0.69, 0.71) 1.69 (1.65, 1.73) <0.001 Unknown 0.71 (0.68, 0.73) 1.69 (1.60, 1.79) <0.001 Summary of Immunotherapy Administered * * * Contraindicated 0.51 (0.45, 0.58) 2.78 (2.30, 3.35) <0.001 Not administered 0.63 (0.55, 0.72) 2.01 (1.66, 2.44) <0.001 Not administered, patient died 0.37 (0.32, 0.43) 4.43 (3.59, 5.47) <0.001 Not part of first course Tx 0.49 (0.48, 0.51) 2.89 (2.78, 3.01) <0.001 Unknown 0.82 (0.71, 0.93) 1.36 (1.12, 1.65) 0.002 Summary of Hormone Therapy Administered * * * Contraindicated 0.56 (0.52, 0.59) 2.43 (2.18, 2.70) <0.001 Not administered 0.54 (0.51, 0.57) 2.53 (2.34, 2.74) <0.001 Not administered, patient died 0.27 (0.22, 0.32) 7.39 (5.58, 9.80) <0.001 Not part of first course Tx 0.66 (0.63, 0.69) 1.88 (1.76, 2.0) <0.001 Unknown 0.54 (0.50, 0.57) 2.55 (2.32, 2.81) <0.001 Coexisting liver metastasis at diagnosis No * * * Not applicable 0.86 (0.61, 1.22) 1.26 (0.74, 2.13) 0.398 Unknown 0.85 (0.81, 0.89) 1.28 (1.20, 1.38) <0.001 Yes 0.63 (0.62, 0.64) 1.99 (1.95, 2.04) <0.001 Abbreviations: NHL, non-Hodgkin lymphoma; NOS, not otherwise specified; Tx, treatment. *Reference group for each respective explanatory variable in the multiple regression model. Whenever a reference category is not specified, the coefficients of the explanatory are compared to either blank values or the excluded group for binary variables (e.g., the reference group for variable “Sex” is “female”). †The acceleration factor or AF in a log-logistic model is interpretable as multiplicative effects on the survival. This suggests that the median life expectancy of each corresponding group is AF times that of the reference group for the respective explanatory. ‡ The proportional odds or PO in a log-logistic model is interpretable as multiplicative effects on the hazard, likewise semiparametric Hazard Ratios. This suggests that the odds of death or hazard for each group is PO times the odds of the reference group for the respective explanatory in the model. Additional Declarations No competing interests reported. Supplementary Files Tablessupplemental.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1559460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":99500276,"identity":"83406a8e-0ddf-4c79-982f-913a22c8c1ab","order_by":0,"name":"Georgios Alexopoulos","email":"data:image/png;base64,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","orcid":"","institution":"Saint Louis University Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Alexopoulos","suffix":""},{"id":99500277,"identity":"ee93f2f9-0738-4309-bff6-8ff919c2d11c","order_by":1,"name":"Justin Zhang","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Zhang","suffix":""},{"id":99500278,"identity":"8143d588-2a22-4be0-b95c-a5cb68f9522e","order_by":2,"name":"Ioannis Karampelas","email":"","orcid":"","institution":"Banner Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Karampelas","suffix":""},{"id":99500279,"identity":"13f24480-b43e-494f-90ae-79c85add900b","order_by":3,"name":"Mayur Patel","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mayur","middleName":"","lastName":"Patel","suffix":""},{"id":99500280,"identity":"6f242d7c-9c64-4f81-ba70-d93820eefa44","order_by":4,"name":"Philippe Mercier","email":"","orcid":"","institution":"Saint Louis University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Mercier","suffix":""}],"badges":[],"createdAt":"2022-04-14 20:59:06","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1559460/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1559460/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20483368,"identity":"baad7a1d-90ac-4ab7-b838-698f5284ac62","added_by":"auto","created_at":"2022-04-19 04:52:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival estimates of NCDB patients presenting with brain metastases (BM) at diagnosis of systemic malignancy stratified by most frequent ICD-O primary topography types.\u003c/strong\u003e The horizontal axis (x-axis) represents time in months following the diagnosis of systemic malignancy, and the vertical axis (y-axis) shows the survival probability. The colored lines represent survival curves of twelve distinct primary topography types along with the respective 95%\u0026nbsp;confidence intervals in colored dashed lines. The number of patients at risk per primary topography immediately before timepoints (t) divided in 4-month intervals is shown in the lower part of the survival plot. Patients at risk did not have the event before time t, and are not censored before or at time t. The\u0026nbsp;\u003cem\u003ep-value\u0026nbsp;\u003c/em\u003eof the Log-Rank test comparing the twelve topography groups is also demonstrated (p \u0026lt;0.0001). The topography prostate provided the strongest overall survival benefit among most frequent sites of origin, as patients with BM from prostate cancer had a 14-month median overall increase in the survival probability. Patients with breast cancer had a survival probability, S(t), of 10.7 months (95%CI[10.2,11.5]). This was followed by melanoma, S(t) = 6.24 months (95%CI[5.98,6.57]) and small cell lung cancer, S(t) = 6.24 months (95%CI[6.14,6.37]). BM from lung cancer/other type, S(t) = 1.8 months (95%CI[1.74,1.84]) and pancreas, S(t) = 2.3 months (95%CI[2.20,2.60]) had the lowest median survival rates among most frequent topography types.\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/618ce04053fab98da69dddd8.png"},{"id":20483326,"identity":"e6594092-dd46-4bee-82c4-b32a19ccc692","added_by":"auto","created_at":"2022-04-19 04:47:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMatrix of Kaplan-Meier survival estimates of \u0026nbsp;NCDB patients presenting with brain metastases (BM) at diagnosis of systemic malignancy stratified by least frequent ICD-O primary topography types.\u003c/strong\u003e The horizontal axis (x-axis) represents time in months following the diagnosis of systemic malignancy, and the vertical axis (y-axis) shows the survival probability. The colored lines represent survival curves of nineteen distinct primary topography types along with the respective 95%\u0026nbsp;confidence intervals in colored dashed lines. The vertical black dotted lines demonstrate the medial survival times for the respective topography types. The topography testicular cancer provided the strongest survival benefit among least frequent sites of BM origin, S(t) = 17.9 months (95%CI[15.2,26.9];p \u0026lt; 0.0001) and the highest median survival in the study. Patients with nodal NHL had a S(t) of 13.6 months (95%CI[10.6,20.3];p \u0026lt; 0.0001). BM from liver S(t) = 2.56 months (95%CI[2.10,3.06]; p \u0026lt; 0.0001), urinary tracts, S(t) = 2.4 months (95%CI[1.87,3.30];p \u0026lt; 0.0001) and the GI tract, S(t) = 2.53 months (95%CI[2.20,2.96];p \u0026lt; 0.0001) had the poorest survival rates among least frequent topography types. NHLnodal, nodal non-Hodgkin lymphoma; NHLExtr, extra-nodal non-Hodgkin lymphoma.\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/dae11a675da37d70cb446fbe.png"},{"id":20483328,"identity":"9483ee3b-5bd5-49a9-9c1e-548f3c667330","added_by":"auto","created_at":"2022-04-19 04:47:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot for univariate Cox proportional hazards model\u003c/strong\u003e. The hazard ratios (HRs) for each respective ICD-O primary topography in cancer patients with brain metastases (BM) are demonstrated. The HRs are interpretable as multiplicative effects on the hazard. Prostate cancer was set as the reference group in the model. BM from liver cancer (HR= 3.10, 95%CI[2.70,3.56]) and pancreas (HR= 3.22, 95%CI[2.87,3.61]) are strongly associated with poor patient survival. The primary topography types including pancreas, liver, biliary tree, and urinary tracts are bad prognostics, while lung (other type) demonstrates the highest hazard (HR= 3.64, 95%CI[3.30,4.01]). Testicular cancer and extra-nodal NHL are good prognostics, when compared to BM from prostate cancer. Unfortunately, frank violations of the PH assumption for multiple covariates in Cox regression make the models unable to generalize (Schoenfeld residuals: p-value \u0026lt; 0.0001).\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/a6454c774417fddbd6b295a8.png"},{"id":20483369,"identity":"c4a81578-1335-4533-b802-85a94e4bb034","added_by":"auto","created_at":"2022-04-19 04:52:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFocused parametric model comparison. \u003c/strong\u003eModel goodness of fit tests by graphical comparison between parametric and non-parametric regression.\u003cstrong\u003e \u003c/strong\u003eOverplotting estimations from non-parametric Kaplan-Meier estimator (black line) versus parametric estimations utilizing exponential (red line), Weibull (blue line), Gompertz (dot-dashed pink line), gamma (green line), generalized gamma (dotted brown line), lognormal (grey line) and log-logistic (dotted orange line) distributions to identify the best survival population pattern to describe the dataset.\u003cstrong\u003e \u003c/strong\u003eThe loglogistic distribution best fits to the non-parametric survival function as shown in the image; the distribution also achieved the lowest AIC = 915163 and Log-likelihood = -457577 among models. Therefore, a log-logistic accelerated failure model can explain the greatest amount of variation in the NCDB population using the fewest possible information. Note that the non-parametric model is closer to the observed data because no function is assumed for the baseline survival probability.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/5e63ec91bc0f6347aab16513.png"},{"id":20483370,"identity":"8540a7c7-c530-4085-8e0c-4c489fc68be5","added_by":"auto","created_at":"2022-04-19 04:52:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":940807,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/c6d0e566-0928-49f2-8927-850709205a80.pdf"},{"id":20483330,"identity":"728fd922-c85f-4eeb-93e2-17df101c82f3","added_by":"auto","created_at":"2022-04-19 04:47:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":679306,"visible":true,"origin":"","legend":"","description":"","filename":"Tablessupplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-1559460/v2/e8f270bfee274c7f05adb6a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostics of systemic malignancy ICD-O topography and morphology types on brain metastases: an NCDB time-to-event cohort\u003c/p\u003e","fulltext":[{"header":"Importance of the study","content":"\n\u003cp\u003eThis study is the largest cohort of adult patients with synchronous brain metastases secondary to an invasive malignancy, as these reported in the National Cancer Database (NCDB). We investigate the combinational interactions between primary topography and morphology types, based on the International Classification for Diseases in Oncology. The results can be summarized as a booklet for prognostic classification of brain metastatic disease, and the study can become a valuable and updated companion to the graded prognostic assessment tool for clinical trial design.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBrain metastases (BM) are the most frequent type of CNS tumor\u0026nbsp;in adults.\u003csup\u003e1-3\u0026nbsp;\u003c/sup\u003ePopulation-based studies report the incidence of metastatic brain cancer ranging from 8.3 to 14.3 per 100 000.\u003csup\u003e2-4 \u0026nbsp;\u003c/sup\u003eOther authors support that up to one-fifth of adult cancer tumors will eventually metastasize to the brain.\u003csup\u003e3,6,7\u0026nbsp;\u003c/sup\u003eThe exact incidence of BM remains\u0026nbsp;unknown, while the reported rates in the literature are estimates at best.\u003csup\u003e1-7\u0026nbsp;\u003c/sup\u003eDespite being a major source of morbidity and mortality, large-scale cohorts examining the prognostics of BMs are lacking.\u003csup\u003e1-6\u0026nbsp;\u003c/sup\u003eMost of the survival data that do exist regarding patients with BM are based on studies decades old having several methodological limitations.\u003csup\u003e3,5,6\u003c/sup\u003e These studies are scarce, with only four population-based reports on BMs having been published recently.\u003csup\u003e2-6\u0026nbsp;\u003c/sup\u003eBetter understanding of the epidemiology and prognostics of BM will help identify individuals who are at greatest risk and guide clinicians in selecting patients who are most likely to benefit from surveillance and prophylaxis.\u003csup\u003e1-6\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe natural history of progression to BM varies according to site (topography) and histology (morphology) of the systemic malignancy.\u003csup\u003e6-11\u003c/sup\u003e Lung cancer, breast cancer, melanoma and colorectal cancer are the most frequent to develop BM, and account for 67%-80% of all BM.\u003csup\u003e2,5,7,10\u003c/sup\u003e A tumor topography-related study in the Detroit metropolitan area from 1973 to 2001 reported that the incidence of BM was highest for lung (19.9%), followed by melanoma (6.9%), renal (6.5%), and breast (5.1%) cancers.\u003csup\u003e12\u0026nbsp;\u003c/sup\u003eThe overall prognosis of BM depends on the primary topography, histology, clinical, and treatment factors.\u003csup\u003e8,10-13\u0026nbsp;\u003c/sup\u003eDespite these key reports, no large cohorts have established a time-to-event analysis implementing the combined effect of primary malignancy topography and morphology types on patient prognosis.\u0026nbsp;In our study, we utilize data from the National Cancer Database of the American Cancer Society,\u003csup\u003e14\u0026nbsp;\u003c/sup\u003eone of the largest hospital-based registries worldwide to\u003csup\u003e\u0026nbsp;\u0026nbsp;\u003c/sup\u003eidentify the prognostics of various systemic malignancy topography and morphology types on BM, based upon the revised guidelines for International Classification of Diseases for Oncology(\u0026lrm;ICD-O)\u0026lrm;.\u003csup\u003e9\u0026nbsp;\u003c/sup\u003eThrough a comprehensive survival analysis workflow, our study is the first to report the combinational interactions of ICD-O primary topography and morphology types on the survival of patients with BM after adjusting for multiple relevant clinical and demographic factors. \u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eData and study population\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData were extracted from the National Cancer Database (NCDB). The NCDB is a\u0026nbsp;joint program of the Commission on Cancer and the American Cancer Society including nationwide data from more than 1,500 Commission-accredited cancer facilities in the United States and Puerto Rico.\u003csup\u003e14\u003c/sup\u003e The entire NCDB adult registry [ages: 18-90+] from year 2010 to 2018 was filtered by \u0026ldquo;CS_METS_DX_BRAIN\u0026rdquo; == \u0026lsquo;YES\u0026rsquo; (Item #: 2852; 2010-2015) OR \u0026ldquo;METS_AT_DX_BRAIN\u0026rdquo; == \u0026lsquo;YES\u0026rsquo; (Item #: 1113; 2016-2018). All patients with BM at diagnosis of an invasive malignancy\u0026nbsp;originating outside the CNS\u0026nbsp;were included. Patients with non-invasive neoplasms were not included in this study.\u0026nbsp;To control for Type S\u0026nbsp;(sign) and Type M (magnitude) errors,\u003csup\u003e15\u003c/sup\u003e we retroactively performed a design analysis and found ICD-O types with small-sample brain metastases (N \u0026lt; 42) resulting in misleading statistically significant estimates. After removing the noisy small-sample sites of origin (Items #: 2852 OR 1113 == \u0026lsquo;YES\u0026rsquo; \u0026lt; 42 patients with BM per topography), we identified a total of 180,325 subjects with BM out of 14,279,749 cancer patients screened (Table1).\u0026nbsp;Based on the ICD-O-3 topographies,\u003csup\u003e9\u003c/sup\u003e 175 patients with BM from extra-nodal NHLs and reported\u0026nbsp;codes C71.0 to C72.9\u0026nbsp;were further excluded, given CNS was identified as the site of origin. The final sample consisted of 180,150 unique observations of cancer patients with BM and 91 variables were extracted from each record including the primary cancer type or topography, tumor histology or morphology, and the detailed anatomic site of origin (supplement). No duplicate patient ID entries were identified. All topography and morphology codes were reported according to ICD-O-3 (first revision).\u003csup\u003e9\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cohort included all cancer patients with BM at diagnosis as reported in the NCDB between the years 2010 and 2018. The target events for this study were the origin site-specific time to death from the time of diagnosis, or death attributable to primary topography, and the histology-specific time to death, or death attributable to morphology. The time origin was set as the point at which a subject was diagnosed with BM, and the time scale was the patient survival in months as reported in the NCDB. We had no reason to suspect informative censoring in a large multicenter database such as the NCDB. The events constituted independent random samples and the subpopulation was screened for duplicate patient entries. Non-parametric analysis was initially utilized to generate unbiased descriptive estimates, in conjunction with semi-parametric or parametric tests whenever necessary. Rank-based tests, such as the log-rank test, were used to statistically test the difference between the Kaplan-Meier survival curves. The semi-parametric Cox Proportional model was used for univariate and multiple regression analysis to estimate the hazard ratios. The proportional hazards (PH) assumption necessitates a constant relationship between the outcome and the covariates over time, and therefore, it is vital for interpretation of the Cox regression. The PH assumption for each predictor in the Cox models was tested calculating the scaled Schoenfeld residuals\u0026nbsp;over time for factors, and the Martingale residuals for continuous variables. Parametric survival models, or accelerated failure time models (AFT), are alternatives to Cox regression, and one of the few available substitutes when the PH assumption is frankly violated.\u003csup\u003e16\u0026nbsp;\u003c/sup\u003eParametric survival analysis in our study included the exponential, Weibull, Gompertz, gamma, generalized gamma, lognormal and log-logistic distributions to identify the best survival population pattern to fit our data. Feature selection was performed using stepwise AIC backward regression by starting from a maximal model including all candidate predictor variables in the study. We used AIC and likelihood ratio tests to assess for relative model goodness of fit followed by the log(-log(S(t))) plots to check for model validity and evaluate the pattern of survival estimates against time. Here, we report the multiple regression analysis results of the best parametric model in conjunction with those extracted from Cox regression.\u003c/p\u003e\n\u003cp\u003eSoftware\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll analyses were implemented using the R statistical software, version 4.1.2. Non-parametric and semiparametric survival approaches were completed using the \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; packages. Feature selection was performed using \u0026ldquo;stepAIC\u0026rdquo; in MASS. Parametric distribution model fit was performed using the \u0026ldquo;flexsurv\u0026rdquo; package, while Kaplan-Meier estimates and the respective effect sizes from parametric bootstrap simulation were generated using the \u0026ldquo;survParamSim\u0026rdquo; implementation in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1) Univariate statistics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.1 Nonparametric survival analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe median topography-specific survival time in cancer patients with BM, or the time when the survival probability, S(t), decreased by 50%, was 17.9 months in tumors originating from the testis (95%CI[15.2,26.9];p \u0026lt; 0.0001). Patients with BM from nodal NHLs had a median survival of 13.6 months (95%CI[10.6,20.3];p \u0026lt; 0.0001), prostate metastases reached a S(t) of 15.8 months (95%CI[12.7,18.3];p \u0026lt; 0.0001) while patients with BM originating from breast had a S(t) of 10.7 months (95%CI[10.2,11.5];p \u0026lt; 0.0001), as shown in Figure 1. The topographies with the lowest median survival times were lung(other type), S(t) = 1.8 months (95%CI[1.74,1.84];p \u0026lt; 0.0001), pancreas, S(t) = 2.3 months (95%CI[2.20,2.60];p \u0026lt; 0.0001), urinary tracts, S(t) = 2.4 months (95%CI[1.87,3.30];p \u0026lt; 0.0001) and liver, S(t) = 2.6 months (95%CI[2.10,3.06];p \u0026lt; 0.0001). The Kaplan-Meier estimates of systemic malignancy topography on patient survival are demonstrated in Figures 1 and 2. When comparing the most common origin sites of BM, as previously reported in the literature,\u003csup\u003e1-6\u003c/sup\u003e there is a continuous survival advantage among patients with prostate cancer by a 14-month median overall increase in the survival probability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median morphology-specific survival time was highest, or 18.4 months (95%CI[13.2,25.4]; p \u0026lt; 0.0001), in patients with infiltrating duct and lobular carcinoma. BM originating from acinar cell carcinomas had a median survival of 16.3 months (95%CI[12.6,18.6];p \u0026lt; 0.0001), while patients with malignant struma ovarii metastatic to the brain achieved an S(t) of 15.2 months (95%CI[13.7,19.3];p \u0026lt; 0.0001). BM originating from malignant neoplasms (ICD-O-3, #8000/3) had the lowest median survival of 1.3 months (95%CI[1.25,1.38];p \u0026lt; 0.0001), followed by spindle cell carcinomas not otherwise specified, S(t) = 2.4 months (95%CI[2.10,3.61];p \u0026lt; 0.0001) and hepatocellular carcinomas, S(t) = 2.7 months (95%CI[2.14,3.19];p \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.2 Cox regression analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe regression beta coefficients along with the hazard ratios (HR) and variable significance based on the topography and morphology of systemic disease were calculated for the variables of interest. Each predictor was assessed through separate Cox regression analysis followed by stratified Cox. The PH assumption was frankly violated for multiple covariates in the NCDB population, and proportionality was unable to be achieved after multiple stratification attempts (supplement).\u003c/p\u003e\n\u003cp\u003eAll the following primary topography types: pancreas, liver, biliary, urinary tracts, lung(other) were associated with poorer survival in patients with BM (Figure3). Tumors originating from testis, nodal NHL, extra-nodal NHL, and prostate were associated with improved survival. Furthermore, BM originating from extra-nodal NHLs reduced the hazard factor by 32% (HR= 0.68, 95%CI[0.52,0.88];p \u0026lt; 0.0001), followed by BM from testis with a HR decrease by 31% (HR= 0.69, 95%CI[0.59,0.81];p \u0026lt; 0.0001) when compared with metastases from prostate cancer (Figure3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the univariate Cox regression, choriocarcinomas showed the best overall survival benefit among all morphology groups. Spindle cell carcinomas not otherwise specified (HR= 9.21, 95%CI[0.39,0.75]; p \u0026lt; 0.0001), hepatocellular carcinomas (HR= 8.21, 95%CI[4.72,14.28]; p \u0026lt; 0.0001), and malignant neoplasms (ICD-O-3 code #8000/3) were poor morphology prognostics in cancer patients with BM (supplement).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2) Multiple regression analysis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1 Feature selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFeature selection started\u0026nbsp;from a full, or saturated, survival model including all 91 variables in the study (supplement). The optimal regression model was the one that minimized the AIC\u0026nbsp;using stepwise backward elimination.\u0026nbsp;The best model to describe the data was the one featuring the seventeen covariates demonstrated in Table2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Semiparametric vs parametric survival analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe fit a Cox model using all the significant covariates from feature selection. The HRs for each respective covariate can be seen in the supplement. The Schoenfeld residuals test was significant for multiple covariates in the model. The non-proportionality was further supported by graphical diagnostics given the log(- log(S(t))) plots did not demonstrate any parallelism (supplement). We were unable to correct for nonproportionality in the Cox model after multiple stratification attempts. We concluded that the estimates derived from utilizing Cox regression in the study should not be generalized, as semiparametric regression led to incorrect inferences.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eAFT models are especially important under such circumstances, given their parametric distribution for the survival times AFT models can make statistical inference accurate and lead to a proper model fitting.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Parametric model fit and results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRelative to other parametric distribution results, the log-logistic distribution achieved the lowest AIC and likelihood ratio tests indicating a more parsimonious model able to better describe the NCDB population survival pattern (Figure4). The log-logistic distribution has a non-monotonic arc-shaped decreasing hazard rate. The absolute parametric model goodness of fit for validity was assessed through Q-Q graphical plots, which demonstrated linearity in a function of time for the loglogistic model. Next, we fit a loglogistic AFT model using all the significant variables from feature selection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.1 Topography and morphology\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe identified the topography \u0026ldquo;prostate\u0026rdquo; as the best overall prognostic among sites of origin in patients with BM (Table2). The median life expectancy for metastatic liver cancer was 10.1 times less (PO= 10.1, 95%CI[6.14,16.5];p \u0026lt; 0.0001) that of BM from prostate. BM from the biliary tree (PO= 8.14, 95%CI[5.37,12.3];p \u0026lt; 0.0001), pancreas (PO= 7.52, 95%CI[6.12,9.24];p \u0026lt; 0.0001), and gallbladder (PO= 7.17, 95%CI[4.67,11.0];p \u0026lt; 0.0001) were associated with poor survival. Similarly, ovarian (PO= 8.48,95%CI[6.09,11.8];p\u0026lt; 0.0001), uterine (PO= 8.49, 95%CI[6.61,10.9];p \u0026lt; 0.0001), and cervical (PO= 8.68, 95%CI[6.65,11.3];p \u0026lt; 0.0001) cancers were poor prognostics. In contrast, patients with BM originating from breast (PO= 3.44, 95%CI[2.81,4.22]; p \u0026lt; 0.0001), bone/joints (PO= 3.22, 95%CI[1.63,6.34];p \u0026lt; 0.0001), and testis (PO= 3.27, 95%CI[1.65,6.45];p \u0026lt; 0.0001) had an improved overall survival second only to that of prostate cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe histology \u0026ldquo;choriocarcinoma\u0026rdquo; has been reported as a potential favorable prognostic in BM,\u003csup\u003e17\u003c/sup\u003e therefore it was utilized as the morphology reference group in the model. Similarly, the histopathology choriocarcinoma was among the best overall morphology prognostics along with mature B-cell lymphomas (Table2). Other good prognostics were carcinoid tumor (PO= 2.72, 95%CI[1.11,6.68];p \u0026lt; 0.0001) and papillary adenocarcinoma (PO= 4.12, 95%CI[1.77,9.58];p \u0026lt; 0.0001). The combined morphology category cystic mucinous and serous carcinomas (ICD-O-3 codes #844-849) was associated with increased survival. In contrast, the median life expectancy for metastatic spindle cell carcinoma was 19.4 times less (PO= 19.4, 95%CI[7.77,48.4];p \u0026lt; 0.0001) that of choriocarcinoma. Hemangiosarcoma was also among the worse prognostics (PO= 18.6, 95%CI[7.3,47.2];p \u0026lt; 0.0001). The remaining morphology groups associated with poor survival were carcinoma undifferentiated (PO= 14.6, 95%CI[5.88,36.4];p \u0026lt; 0.0001), Ewing sarcoma \u0026nbsp;(PO= 14.0, 95%CI[4.55,43.3];p \u0026lt; 0.0001), malignant neoplasm (PO= 15.9, 95%CI[6.91,36.7];p \u0026lt; 0.0001), pseudosarcomatous carcinoma (PO= 13.8, 95%CI[5.92,32.2];p \u0026lt; 0.0001), renal cell carcinoma/sarcomatoid (PO= 14.0, 95%CI[5.81,33.9];p \u0026lt; 0.0001), signet ring cell carcinoma (PO= 12.2, 95%CI[5.22,28.6];p \u0026lt; 0.0001), spindle cell sarcoma (PO= 14.5, 95%CI[5.34,39.2];p \u0026lt; 0.0001) and squamous cell carcinoma from spindle cells (PO= 13.5, 95%CI[4.97,36.5];p \u0026lt; 0.0001). Tumors in the combined morphology category osseous and chondromatous neoplasms (ICD-O-3 codes #918-924) were poor prognostics (Table2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.2 Demographics and patient specific factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe median survival for males was 1.24 times less that of females when adjusting for other covariates in the model (PO= 1.24, 95%CI[1.22,1.27];p \u0026lt; 0.0001). Increasing patient age, White race, and American Indians or Eskimos were bad prognostics (Table2). Asian race demonstrated a protective effect as the survival was accelerated by a factor of 1.3 (AF= 1.3, 95%CI[1.25,1.36];p \u0026lt; 0.0001) when compared to Black patients. Increasing Charlson-Deyo comorbidity score (CDScore) was associated with poor survival, while patients with a CDscore of 3 had the worse overall prognosis (PO= 1.52, 95%CI[1.45,1.59];p \u0026lt; 0.0001) among other CDscore groups. The median patient income and level of education did not achieve significance in the model. In patients with coexisting liver metastatic disease the median life expectancy was decreased by half (PO= 1.99, 95%CI[1.95,2.04];p \u0026lt; 0.0001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.3 Type of treatment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients with BM who underwent surgery of the primary site with no residual tumor margins had 2.07 times increased overall survival (AF= 2.07, 95%CI[1.72,2.49];p \u0026lt; 0.0001). Failure of administering chemotherapy, despite being part of first course treatment, decreased the median survival by 4.31 (PO= 4.31, 95%CI[4.13,4.49];p \u0026lt; 0.0001). Similarly, no administration of radiotherapy (PO= 1.59, 95%CI[1.5,1.68];p \u0026lt; 0.0001), immunotherapy (PO= 2.01, 95%CI[1.66,2.44]; p \u0026lt; 0.0001) and hormone therapy (PO= 2.53, 95%CI[2.34,2.74];p \u0026lt; 0.0001) were all associated with shorter survival times.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBM continue to foreshadow a poor prognosis for all cancer patients.\u003csup\u003e1-6\u0026nbsp;\u003c/sup\u003eIn 80% of the cases, BM are discovered in a metachronous fashion, but less frequently, BM can be diagnosed at the same time as the systemic malignancy (synchronous diagnosis).\u003csup\u003e5-8\u003c/sup\u003e The sources of BM(in descending order) are cancers of the lung, breast, skin, kidney, and gastrointestinal (GI) tract.\u003csup\u003e\u0026nbsp;2,5,7,10\u003c/sup\u003e In the NCDB, all lung cancer morphology types demonstrated an increased frequency for synchronous BM with an approximately 9% of small cell lung cancer (SCLC) patients presenting with BM at the diagnosis of systemic disease (Table1). Previous studies have also reported 10% of SCLC patients overall presenting with synchronous BM.\u003csup\u003e1-4,21\u0026nbsp;\u003c/sup\u003eFurthermore, GI tract malignancies in the study demonstrated an increased frequency of synchronous BM, especially patients with primary esophageal cancer (1.1%). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prognosis of BM varies depending on the primary topography, morphology, treatment status, and key patient factors.\u003csup\u003e1-8\u003c/sup\u003e The graded and upgraded prognostic assessments (GPA) remain some of the most valuable prognostic tools for common histologic types of BM.\u003csup\u003e13,18,19 \u0026nbsp;\u003c/sup\u003eThe Radiation Therapy Oncology Group had previously developed a prognostic classification system for BM patients based on recursive partitioning analysis of performance status, age, and systemic tumor activity.\u003csup\u003e20\u003c/sup\u003e Large-scale studies reporting\u0026nbsp;the combinational interactions of various prognostics on the survival of BM patients are still lacking.\u003csup\u003e1-8\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCagney et al utilized population-based data to identify 26,430 patients with synchronous BM and analyzed the incidences of various topographies; the authors reported BM secondary to prostate and breast cancer as having the longest median survival times.\u003csup\u003e10\u003c/sup\u003e We identified\u0026nbsp;prostate cancer and breast cancer providing the strongest survival benefit among other topography types, while further reporting BM originating from bone/joints and testicular cancer as favorable prognostics. Gynecologic and GI malignancies with synchronous BM, especially those originating from liver, pancreas, uterus, cervix, and the ovaries demonstrated the poorest survival in our study. A recent systematic review reported that BM from cervical cancer could reduce longevity independent of overall tumor burden.\u003csup\u003e22\u003c/sup\u003e Our cohort is the first study to calculate the adjusted effect sizes between the various ICD-O topography types and further provide evidence to support such a statement (Table2).\u0026nbsp;Scant single institution reports have identified patients with BM originating from choriocarcinoma and carcinoid tumors as potential long-term survivors.\u003csup\u003e17,23,24\u003c/sup\u003e Here, we showed how the histology choriocarcinoma was the most influential among other morphology types, followed by carcinoid tumor and mature B-cell lymphomas (Table2). Our study is also the first in the literature to identify various spindle cell-derived malignancies as poor prognostics, such as, spindle cell carcinoma, spindle cell sarcoma and squamous cell carcinoma from spindle cells. Choi et al reported the sarcomatoid histology component in BM from renal cell carcinomas(RCC) as a bad prognostic,\u003csup\u003e25\u0026nbsp;\u003c/sup\u003e we further expand the authors\u0026rsquo; statement to the sarcomatoid component in BM from RCC\u0026nbsp;is a poor predictor of survival among all ICD-O morphologies. BM from GI signet ring cell carcinomas are extremely rare;\u003csup\u003e26\u003c/sup\u003e here we first identified the morphology as a bad prognostic. Pediatric BM from Ewing sarcoma carry a grave prognosis,\u003csup\u003e27\u0026nbsp;\u003c/sup\u003ebut adult BM from Ewing sarcoma also demonstrated a poor survival in our study. Favorable demographic factors for enhanced BM survival included female sex, Asian race and lower\u0026nbsp;CDscore (Table2). Several efficacious therapeutic options were associated with improved survival in our cohort including surgery of the primary site\u0026nbsp;without residual tumor margins, and, whenever part of first course treatment, the administration of radiotherapy, chemotherapy, immunotherapy, and hormone therapy; our findings here are in concordance with previous literature.\u003csup\u003e1-8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe accurate and generalizable estimation of effect sizes of the various influential survival predictors in cancer patients with BM is important for clinical trial design.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThis NCDB study is the largest published cohort, while our multiple regression model also provides an unbiased estimate of the various\u0026nbsp;ICD-O topography and morphology effect sizes by simultaneously adjusting for multiple other known predictors. We provide an important updated companion to the\u0026nbsp;GPA tool which would allow clinicians to estimate survival, individualize treatment, and stratify clinical trials in patients with BM based on individual\u0026nbsp;ICD-O topography and morphology types.\u0026nbsp;Furthermore, our study would help organize important clinical information and risk factors, leading to better identification, surveillance, improved patient counseling, more rigorous prognostic classification, and prophylactic treatment of cancer patients at greatest risk for BM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThe primary limitations of this study are its integral data quality and the retrospective design. Although all essential factors were extracted from the NCDB to mitigate the risk of confounding, the possibility of influence from unmeasured confounders cannot be excluded. Real-world data are highly complex and an incomplete reflection of reality. There is always a chance for introduction of unpredictable outliers even with basic structural data collection. In concordance with previous literature, all survival predictors were included for feature selection except for the Karnofsky Performance Scale, as no such performance status variable has been reported in the NCDB. Nevertheless, the patient performance status is indirectly reflected by the administration of radiotherapy and systemic therapy in the database, covariates for which the model was appropriately adjusted. The NCDB is a hospital-based registry, therefore the defined populations and hospitals are subject to (and limited by) the regional referral patterns, regional access to health care and cancer treatment, and the inherent sampling biases of the pathology of that region. In addition, no two regions have equivalent treatment expertise. Slight variations in clinical aggressiveness in obtaining diagnostic imaging and/or surgery or even the frequency of biopsies potentially affect the reported incidences of BM. Randomized controlled trials would be ideal; however, it is neither practical nor feasible to establish a cohort on this scale. In addition, it is ethically unjustifiable to randomize newly diagnosed BM patients to a no-treatment placebo arm to assess for covariate significance.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Cancer Database which is a joint project of the American Cancer Society and the Commission on Cancer of the American College of Surgeons for providing the data used in this study. The NCDB, established in 1989, is a nationwide, facility-based, comprehensive clinical surveillance resource oncology data set that currently captures 72% of all newly diagnosed malignancies in the US annually. The American College of Surgeons and the Commission on Cancer are not responsible for the analytic or statistical methodology employed in the study. We are indebted to many clinicians for their contributions to this registry, and to the patients for participation in research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Philippe Mercier was supported by a Saint Louis University College of Medicine Clinical Research Scholarship. Statistical assistance was provided by Noor Al-Hammadi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design:\u0026nbsp;Georgios Alexopoulos\u003c/p\u003e\n\u003cp\u003eProvision of study materials or patients: \u0026nbsp;Philippe Mercier, Georgios Alexopoulos\u003c/p\u003e\n\u003cp\u003eCollection and assembly of data:\u0026nbsp;Georgios Alexopoulos, Justin Zhang\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation:\u0026nbsp;Georgios Alexopoulos\u003c/p\u003e\n\u003cp\u003eManuscript writing:\u0026nbsp;Georgios Alexopoulos\u003c/p\u003e\n\u003cp\u003eAssisted with manuscript writing: Justin Zhang, Mayur Patel, Ioannis Karampelas\u003c/p\u003e\n\u003cp\u003eRevised the final manuscript: Georgios Alexopoulos, Ioannis Karampelas, Philippe Mercier\u003c/p\u003e\n\u003cp\u003eFinal approval of manuscript:\u0026nbsp;All authors\u003c/p\u003e\n\u003cp\u003eAccountable for all aspects of the work:\u0026nbsp;All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets generated and analysed during the current study are available in the\u0026nbsp;\u003c/em\u003eNational Cancer Database (\u003cem\u003eNCDB) repository,\u0026nbsp;\u003c/em\u003e\u003ca href=\"https://ncdbapp.facs.org/puf/\"\u003ehttps://ncdbapp.facs.org/puf/\u003c/a\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis is an observational study. The XYZ Research Ethics Committee has confirmed that no ethical approval is required.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u0026nbsp;\u003c/strong\u003eNone declared.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no potential conflicts of interest related to this study. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFox BD, Cheung VJ, Patel AJ, Suki D, Rao G. Epidemiology of metastatic brain tumors. Neurosurg Clin N Am. 2011 Jan;22(1):1-6, v. doi: 10.1016/j.nec.2010.08.007. PMID: 21109143.\u003c/li\u003e\n \u003cli\u003eNayak L, Lee EQ, Wen PY. Epidemiology of brain metastases. Curr Oncol Rep. 2012 Feb;14(1):48-54. doi: 10.1007/s11912-011-0203-y. PMID: 22012633.\u003c/li\u003e\n \u003cli\u003eSacks P, Rahman M. Epidemiology of Brain Metastases. Neurosurg Clin N Am. 2020 Oct;31(4):481-488. doi: 10.1016/j.nec.2020.06.001. PMID: 32921345.\u003c/li\u003e\n \u003cli\u003eOstrom QT, Wright CH, Barnholtz-Sloan JS. Brain metastases: epidemiology. Handb Clin Neurol. 2018;149:27-42. doi: 10.1016/B978-0-12-811161-1.00002-5. PMID: 29307358.\u003c/li\u003e\n \u003cli\u003eLamba N, Wen PY, Aizer AA. Epidemiology of brain metastases and leptomeningeal disease. Neuro Oncol. 2021 Sep 1;23(9):1447-1456. doi: 10.1093/neuonc/noab101. PMID: 33908612; PMCID: PMC8408881.\u003c/li\u003e\n \u003cli\u003eLin X, DeAngelis LM. Treatment of Brain Metastases. J Clin Oncol. 2015 Oct 20;33(30):3475-84. doi: 10.1200/JCO.2015.60.9503. Epub 2015 Aug 17. PMID: 26282648; PMCID: PMC5087313.\u003c/li\u003e\n \u003cli\u003eLowery FJ, Yu D. Brain metastasis: Unique challenges and open opportunities. \u003cem\u003eBiochim Biophys Acta Rev Cancer\u003c/em\u003e. 2017;1867(1):49-57. doi:10.1016/j.bbcan.2016.12.001\u003c/li\u003e\n \u003cli\u003eValiente M, Ahluwalia MS, Boire A, et al. The Evolving Landscape of Brain Metastasis. \u003cem\u003eTrends Cancer\u003c/em\u003e. 2018;4(3):176-196. doi:10.1016/j.trecan.2018.01.003\u003c/li\u003e\n \u003cli\u003eWorld Health Organization.\u0026nbsp;(\u0026lrm;2013)\u0026lrm;.\u0026nbsp;International classification of diseases for oncology (\u0026lrm;ICD-O)\u0026lrm;, 3rd ed., 1st revision.\u0026nbsp;World Health Organization.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCagney DN, Martin AM, Catalano PJ, Redig AJ, Lin NU, Lee EQ, Wen PY, Dunn IF, Bi WL, Weiss SE, Haas-Kogan DA, Alexander BM, Aizer AA. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro Oncol. 2017 Oct 19;19(11):1511-1521. doi: 10.1093/neuonc/nox077. PMID: 28444227; PMCID: PMC5737512.\u003c/li\u003e\n \u003cli\u003eG\u0026aacute;llego P\u0026eacute;rez-Larraya J, Hildebrand J. Brain metastases. Handb Clin Neurol. 2014;121:1143-57. doi: 10.1016/B978-0-7020-4088-7.00077-8. PMID: 24365409.\u003c/li\u003e\n \u003cli\u003eBarnholtz-Sloan JS, Sloan AE, Davis FG, Vigneau FD, Lai P, Sawaya RE. Incidence proportions of brain metastases in patients diagnosed (1973 to 2001) in the Metropolitan Detroit Cancer Surveillance System. J Clin Oncol. 2004 Jul 15;22(14):2865-72. doi: 10.1200/JCO.2004.12.149. PMID: 15254054\u003c/li\u003e\n \u003cli\u003eSperduto PW, Kased N, Roberge D, Xu Z, Shanley R, Luo X, Sneed PK, Chao ST, Weil RJ, Suh J, Bhatt A, Jensen AW, Brown PD, Shih HA, Kirkpatrick J, Gaspar LE, Fiveash JB, Chiang V, Knisely JP, Sperduto CM, Lin N, Mehta M. Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin Oncol. 2012 Feb 1;30(4):419-25. doi: 10.1200/JCO.2011.38.0527. Epub 2011 Dec 27. PMID: 22203767; PMCID: PMC3269967.\u003c/li\u003e\n \u003cli\u003eAmerican College of Surgeons. National Cancer Database.\u0026nbsp;\u003ca href=\"https://www-facs-org.ezp.slu.edu/quality-programs/cancer/ncdb\"\u003ehttps://www-facs-org.ezp.slu.edu/quality-programs/cancer/ncdb\u003c/a\u003e. Accessed March 08, 2022.\u003c/li\u003e\n \u003cli\u003eGelman A, Carlin J. Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspect Psychol Sci. 2014 Nov;9(6):641-51. doi: 10.1177/1745691614551642. PMID: 26186114.\u003c/li\u003e\n \u003cli\u003eAlexopoulos G, Zhang J, Karampelas I, Patel M, Kemp J, Coppens J, Mattei TA, Mercier P. Long-term time series forecasting and updates on survival analysis of glioblastoma multiforme, a 1975-2018 population-based study. Neuroepidemiology. 2022 Feb 16. doi: 10.1159/000522611. Epub ahead of print. PMID: 35172317.\u003c/li\u003e\n \u003cli\u003eAthanassiou A, Begent RH, Newlands ES, Parker D, Rustin GJ, Bagshawe KD. Central nervous system metastases of choriocarcinoma. 23 years\u0026apos; experience at Charing Cross Hospital. Cancer. 1983 Nov 1;52(9):1728-35. doi: 10.1002/1097-0142(19831101)52:9\u0026lt;1728::aid-cncr2820520929\u0026gt;3.0.co;2-u. PMID: 6684500.\u003c/li\u003e\n \u003cli\u003eSperduto PW, Chao ST, Sneed PK, Luo X, Suh J, Roberge D, Bhatt A, Jensen AW, Brown PD, Shih H, Kirkpatrick J, Schwer A, Gaspar LE, Fiveash JB, Chiang V, Knisely J, Sperduto CM, Mehta M. Diagnosis-specific prognostic factors, indexes, and treatment outcomes for patients with newly diagnosed brain metastases: a multi-institutional analysis of 4,259 patients. Int J Radiat Oncol Biol Phys. 2010 Jul 1;77(3):655-61. doi: 10.1016/j.ijrobp.2009.08.025. Epub 2009 Nov 26. PMID: 19942357.\u003c/li\u003e\n \u003cli\u003eSperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, Nesbit E, Kruser TJ, Chan J, Braunstein S, Lee J, Kirkpatrick JP, Breen W, Brown PD, Shi D, Shih HA, Soliman H, Sahgal A, Shanley R, Sperduto WA, Lou E, Everett A, Boggs DH, Masucci L, Roberge D, Remick J, Plichta K, Buatti JM, Jain S, Gaspar LE, Wu CC, Wang TJC, Bryant J, Chuong M, An Y, Chiang V, Nakano T, Aoyama H, Mehta MP. Survival in Patients With Brain Metastases: Summary Report on the Updated Diagnosis-Specific Graded Prognostic Assessment and Definition of the Eligibility Quotient. J Clin Oncol. 2020 Nov 10;38(32):3773-3784. doi: 10.1200/JCO.20.01255. Epub 2020 Sep 15. PMID: 32931399; PMCID: PMC7655019.\u003c/li\u003e\n \u003cli\u003eGaspar L, Scott C, Rotman M, Asbell S, Phillips T, Wasserman T, McKenna WG, Byhardt R. Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys. 1997 Mar 1;37(4):745-51. doi: 10.1016/s0360-3016(96)00619-0. PMID: 9128946.\u003c/li\u003e\n \u003cli\u003eWaqar SN, Samson PP, Robinson CG, Bradley J, Devarakonda S, Du L, Govindan R, Gao F, Puri V, Morgensztern D. Non-small-cell Lung Cancer With Brain Metastasis at Presentation. Clin Lung Cancer. 2018 Jul;19(4):e373-e379. doi: 10.1016/j.cllc.2018.01.007. Epub 2018 Mar 9. PMID: 29526531; PMCID: PMC6990432\u003c/li\u003e\n \u003cli\u003eTakayanagi A, Florence TJ, Hariri OR, Armstrong A, Yazdian P, Sumida A, Quadri SA, Cohen J, Tehrani OS. Brain metastases from cervical cancer reduce longevity independent of overall tumor burden. Surg Neurol Int. 2019 Sep 13;10:176. doi: 10.25259/SNI_37_2019. PMID: 31583173; PMCID: PMC6763668.\u003c/li\u003e\n \u003cli\u003eSpears WT, Morphis JG 2nd, Lester SG, Williams SD, Einhorn LH. Brain metastases and testicular tumors: long-term survival. Int J Radiat Oncol Biol Phys. 1992;22(1):17-22. doi: 10.1016/0360-3016(92)90977-p. PMID: 1370066.\u003c/li\u003e\n \u003cli\u003eHlatky R, Suki D, Sawaya R. Carcinoid metastasis to the brain. Cancer. 2004 Dec 1;101(11):2605-13. doi: 10.1002/cncr.20659. PMID: 15495181.\u003c/li\u003e\n \u003cli\u003eChoi SY, Yoo S, You D, Jeong IG, Song C, Hong B, Hong JH, Ahn H, Kim CS. Prognostic\u0026nbsp;Factors for Survival of Patients With Synchronous or Metachronous Brain Metastasis of Renal Cell Carcinoma. Clin Genitourin Cancer. 2017 Dec;15(6):717-723. doi: 10.1016/j.clgc.2017.05.010. Epub 2017 May 10. PMID: 28552571.\u003c/li\u003e\n \u003cli\u003eAli S, Khan MT, Idrisov EA, Maqsood A, Asad-Ur-Rahman F, Abusaada K. Signet Cell in the Brain: A Case Report of Leptomeningeal Carcinomatosis as the Presenting Feature of Gastric Signet Cell Cancer. Cureus. 2017 Mar 7;9(3):e1085. doi: 10.7759/cureus.1085. PMID: 28405535; PMCID: PMC5384845.\u003c/li\u003e\n \u003cli\u003eParasuraman S, Langston J, Rao BN, Poquette CA, Jenkins JJ, Merchant T, Cain A, Pratt CB, Pappo AS. Brain metastases in pediatric Ewing sarcoma and rhabdomyosarcoma: the St. Jude Children\u0026apos;s Research Hospital experience. J Pediatr Hematol Oncol. 1999 Sep-Oct;21(5):370-7. doi: 10.1097/00043426-199909000-00007. PMID: 10524449.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Abbreviations","content":"AF, acceleration factor; AFT, accelerated failure time; AIC, Akaike information criterion; BM, brain metastases; CDscore, Charlson-Deyo comorbidity score; GI, gastrointestinal; GPA, graded prognostic assessment; HR, hazard ratio; ICD-O, International Classification of Diseases for Oncology; NCDB, National Cancer Database; NHL, Non-Hodgkin lymphoma; PH, proportional hazards; PO, proportional odds; RCC, renal cell carcinoma; SCLC small cell lung cancer."},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Frequency Tables. Incidence of Cancer Patients in the National Cancer Database (NCDB) with Synchronous Brain Metastases stratified by ICD-O Primary Topography from year 2010 to 2018.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-O Primary Topography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.35785007072136%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal N Cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain Metastases at diagnosis (N,%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eAnus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e78,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;64 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eBiliary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e108,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e225(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eBone/Joint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e28,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;86 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e3,208,696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e7,077 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eCervix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e148,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;317 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e1,490,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;2,814 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eDigestive tract, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e16,905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e385 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eEsophagus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e181,586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;2,027 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eFemale genitals/placenta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e30,428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e89 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eGallbladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e41,967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;87 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e615,519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;5,106 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e233,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;506 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eLung, non-small cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e1,772,978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;109,782 (6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eLung, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e157,582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;11,554 (7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eLung, small cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e296,583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;26,359 (8.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eMelanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e704,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;6,902 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eNasal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e27,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;150 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eNHL, extra-nodal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e207,804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;175 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eNHL, nodal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e442,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;514 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eOvary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e245,465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;258 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003ePancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e459,547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;1,178 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eSkin, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e47,204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e56 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eProstate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e1,742,871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;720 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eSmall Intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e87,435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;137 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eSoft Tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e119,330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;513 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eStomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e237,704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;955 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eTestis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e83,903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;534 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eTrachea/Larynx/Mediastinum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e150,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;130 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eUrinary Tracts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e46,586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;103 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eUrinary Bladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e671,462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;520 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003eUterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e596,088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u0026nbsp;827 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"32.95615275813296%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Number of Patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.35785007072136%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14,279,749\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.68599717114569%\"\u003e\n \u003cp\u003e\u003cstrong\u003e180,150(1.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Parametric Survival Analysis. Accelerated Failure Time Model Using the Best Distribution (Log-logistic) to Describe the Survival Pattern in the Population, NCDB from 2010-2018 , N = 145,429\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcceleration Factor\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportional Odds\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-O topography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Anus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.32 (0.22, 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.71 (3.26, 9.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Biliary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.25 (0.19, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.14 (5.37, 12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Bone/joint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.46 (0.29, 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.22 (1.63, 6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Breast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.44 (0.39, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.44 (2.81, 4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Cervix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.20, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.68 (6.65, 11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.32 (0.29, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.50 (4.55, 6.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Digestive, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.30 (0.25, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.27 (4.81, 8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Esophagus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.30 (0.26, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.20 (5.11, 7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female genitals/placenta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.22, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.91 (3.49, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Gallbladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.20, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.17 (4.67, 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Kidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.27, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.91 (4.71, 7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.22 (0.16, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.1 (6.14, 16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lung, non-small cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.36 (0.32, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.77 (3.99, 5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lung, other type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.27, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.88 (4.83, 7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lung, small cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.40 (0.33, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.02 (2.99, 5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.29 (0.22, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.52 (4.28, 9.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Nasal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.39 (0.31, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.06 (2.77, 5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;NHL Extra-nodal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.14 (0.03, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e19.2 (2.25, 164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;NHL Nodal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.09 (0.02, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e34.8 (4.08, 296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ovary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.19, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.48 (6.09, 11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.26 (0.23, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.52 (6.12, 9.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Skin, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.43 (0.29, 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.62 (1.97, 6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Small Intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.24, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.94 (4.12, 8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Soft Tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.34 (0.27, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.10 (3.61, 7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.24, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.20 (5.81, 8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Testis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.46 (0.29, 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.27 (1.65, 6.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Trachea/Larynx/Mediastinum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.38 (0.27, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.35 (2.58, 7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urinary Tracts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.28 (0.21, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.58 (4.20, 10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urinary Bladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.29 (0.24, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.44 (4.77, 8.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Uterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.21, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.49 (6.61, 10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Academic/Research Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.06 (1.01, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.91 (0.84, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.035\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Community Cancer Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.90 (0.85, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.17 (1.07, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Comprehensive Community Cancer Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.92 (0.87, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.14 (1.05, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Integrated Cancer Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.92(0.87, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.13 (1.03, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.99 (0.988, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.02 (1.02, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026mdash;Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.87 (0.855, 0.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.24 (1.22, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;American Indian or Eskimo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.89 (0.80, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.18 (1.01, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.041\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.30 (1.25, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.67 (0.63, 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.22 (1.13, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.74 (0.66, 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.16 (1.01, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.79 (0.64, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.036\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.12 (1.04, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.84 (0.75, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.92 (0.90, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.13 (1.10, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Income (2016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;$30,000 to 34,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.96 (0.85, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.06 (0.87, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.580\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;$35,000 to 45,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.99 (0.87, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.02 (0.83, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.877\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Above $46,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.08 (0.95, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.89 (0.73, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.227\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Less $30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.92 (0.81, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.13 (0.93, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.214\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban or Rural (2013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.95 (0.91, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.09 (1.02, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.91 (0.86, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.15 (1.05, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.93 (0.89, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.12 (1.05, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh school degree (2016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;10.9% to 17.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.97 (0.84, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.05 (0.85, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.647\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;6.3% to 10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.95 (0.82, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.08 (0.87, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.497\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Above 17.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.03 (0.89, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.95 (0.77, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.662\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Less 6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.97 (0.84, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.05 (0.85, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.640\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Charlson-Deyo Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.87 (0.85, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.24 (1.22, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.80 (0.79, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.38 (1.34, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.76 (0.74, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.52 (1.45, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGreat Circle Distance (miles)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1 (1, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1 (1, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-O morphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Choriocarcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Acinar cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.39 (0.22, 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.23 (1.81, 9.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Acinar cell cystadenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.15 (0.02, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e18.1 (0.99, 328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.050\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Adenocarcinomas, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.15, 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.44 (3.24, 17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Adenosquamous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.22 (0.13, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.95 (4.31, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Adnexal and skin appendage neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.09, 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.0 (3.74, 38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Atypical carcinoid tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.35 (0.19, 0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.83 (1.91, 12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Basal cell carcinomas, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.11 (0.02, 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e27.7 (1.53, 501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.024\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Basaloid squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.13, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.67 (3.50, 21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Blood vessel tumors, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.45 (0.17, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.34 (0.78, 14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.103\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Carcinoid tumor, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.52 (0.28, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.72 (1.11, 6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.029\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Carcinoma, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.12, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.9 (4.74, 25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Carcinoma, undifferentiated, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.17 (0.09, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e14.6 (5.88, 36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Carcinosarcoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.22 (0.12, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.82 (4.02, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Cholangiocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.15, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.17 (2.80, 18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Clear cell adenocarcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.33 (0.19, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.46 (2.34, 12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;CNS embryonal tumor, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.10 (0.02, 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e32.6 (3.71, 287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Combined small cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.12, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.6 (4.39, 25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Complex epithelial neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.09, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.3 (4.51, 33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Complex mixed and stromal neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.10, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.7 (4.90, 33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Cystic, mucinous, and serous carcinomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.53 (0.26, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.61 (0.90, 7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.076\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ductal and lobular carcinomas, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.12, 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.77 (3.33, 23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Endometrioid adenocarcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.23 (0.13, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.21 (3.84, 22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ewing sarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.17 (0.08, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e14.0 (4.55, 43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Fibroepithelial neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.08 (0.02, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e42.7 (6.42, 284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Fibromatous neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.22 (0.11, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.2 (3.57, 28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Germ cell neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.07 (0.02, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e56.0 (11.8, 267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Giant cell tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.12 (0.02, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e24.7 (2.64, 231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Granular cell tumors and alveolar soft part sarcomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.71 (0.32, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.69 (0.51, 5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.390\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hemangiosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.14 (0.08, 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e18.6 (7.30, 47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hepatocellular carcinoma, all types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.26 (0.14, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.53 (2.86, 19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hepatoid adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.09, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e11.7 (4.21, 32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Infiltrating duct and lobular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.29 (0.16, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.42 (2.64, 15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Infiltrating duct carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.13, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.93 (3.86, 20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Infiltrating duct mixed with other types of carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.17, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.78 (2.26, 14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Inflammatory carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.10, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.7 (5.10, 31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Large cell carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.12, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.8 (4.66, 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Large cell neuroendocrine carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.23 (0.13, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.96 (3.88, 20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Leiomyosarcoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.14, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.25 (2.87, 18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lepidic adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.32 (0.18, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.57 (2.35, 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Leukemias, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e2.70 (0.56, 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.22 (0.02, 2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.216\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lipomatous sarcomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.25 (0.11, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.38 (2.62, 26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lobular carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.25 (0.14, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.32 (3.52, 19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lymphoid leukemias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.82 (0.17, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.35 (0.12, 14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.808\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant lymphoma, diffuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.41 (0.32, 6.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.59 (0.06, 5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.646\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant lymphoma, large B-cell, diffuse, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.84 (0.22, 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.31 (0.18, 9.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.790\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant lymphoma, non-Hodgkin, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.33 (0.33, 5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.65 (0.08, 5.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.683\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant melanoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.38 (0.21, 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.33 (1.74, 10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant peripheral nerve sheath tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.09, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.7 (3.99, 40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malignant tumor, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.31 (0.15, 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.82 (1.96, 17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mature B-cell lymphomas, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e2.10 (0.55, 8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.32 (0.04, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.279\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mature T- and NK-cell lymphomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.63 (0.16, 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.03 (0.25, 16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.508\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Melanoma, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.38 (0.20, 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.38 (1.70, 11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Miscellaneous tumors, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.47 (0.07, 3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.15 (0.18, 56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.436\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mixed cell adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.32 (0.17, 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e5.56 (2.16, 14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mucin-producing adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.26 (0.15, 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.53 (3.17, 17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mucinous adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.14, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.75 (3.78, 20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mucoepidermoid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.26 (0.12, 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.47 (2.19, 25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mullerian mixed tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.23 (0.11, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.03 (3.08, 26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Myomatous neoplasms, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.15 (0.07, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e17.8 (6.31, 50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Myxomatous neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e2.20 (0.41, 11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.30 (0.02, 3.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.360\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Neoplasm, malignant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.16 (0.09, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e15.9 (6.91, 36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Neuroendocrine carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.24 (0.14, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.39 (3.64, 19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Neuroepitheliomatous neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.78 (0.30, 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.45 (0.33, 6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.624\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Nodular melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.25 (0.14, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.95 (3.11, 20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Non-small cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.12, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.6 (4.60, 24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Oat cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.20 (0.11, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e11.1 (4.35, 28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Osseous and chondromatous neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.14 (0.07, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e19.5 (6.34, 59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Papillary adenocarcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.39 (0.22, 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.12 (1.77, 9.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Papillary carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.25 (0.13, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e8.14 (3.10, 21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Papillary transitional cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.26 (0.14, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.53 (2.97, 19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Paragangliomas and glomus tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e3.56 (0.52, 24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.15 (0.01, 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.195\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Plasma cell tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1 (1, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1 (1, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pleomorphic carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.10, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.7 (5.19, 30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pseudosarcomatous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.18 (0.10, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e13.8 (5.92, 32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Renal cell carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.15, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.19 (3.08, 16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Renal cell carcinoma, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.23 (0.10, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.26 (2.83, 30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Renal cell carcinoma, sarcomatoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.17 (0.09, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e14.0 (5.81, 33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Serous carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.29 (0.16, 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.34 (2.46, 16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Signet ring cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.19 (0.11, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.2 (5.22, 28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Small cell carcinoma, intermediate cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.10, 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.8 (3.75, 31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Small cell carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.20 (0.11, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e11.1 (4.68, 26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Soft tissue sarcomas, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.18 (0.10, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.8 (5.30, 31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Solid carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.30 (0.16, 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.15 (2.47, 15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Specialized gonadal neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.07 (0.01, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e53.2 (2.90, 975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Spindle cell carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.14 (0.07, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e19.4 (7.77, 48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Spindle cell melanoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.43 (0.21, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e3.61 (1.25, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Spindle cell sarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.17 (0.09, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e14.5 (5.34, 39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Squamous cell carcinoma, keratinizing, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.20 (0.12, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.7 (4.61, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Squamous cell carcinoma, large cell, non-keratinizing, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.22 (0.13, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.72 (4.12, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Squamous cell carcinoma, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.21 (0.12, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e10.6 (4.62, 24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Squamous cell carcinoma, spindle cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.18 (0.09, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e13.5 (4.97, 36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Squamous cell neoplasms, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.18 (0.09, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e13.1 (4.71, 36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Struma ovarii, malignant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.20 (0.10, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e11.6 (4.35, 30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Synovial sarcomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.16 (0.08, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e15.5 (5.10, 47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Thymic epithelial neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.14 (0.02, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e18.5 (1.03, 335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.048\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Transitional cell carcinomas, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.23 (0.13, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e9.09 (3.83, 21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Trophoblastic neoplasms, other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.53 (0.15, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.56 (0.39, 16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.329\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Surgical Margins (surgery at primary site)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Macroscopic residual tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Margins not evaluable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.97 (0.79, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.04 (0.76, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.800\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Microscopic residual tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.31 (1.06, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.66 (0.48, 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No primary site surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.84 (0.67, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.31 (0.99, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.053\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No residual tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e2.07 (1.72, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.33 (0.25, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Residual tumor, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.13 (0.93, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.83 (0.61, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.218\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e1.38 (1.14, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e0.61 (0.46, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Chemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Contraindicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.30 (0.29, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e6.30 (6.03, 6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.38 (0.37, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.31 (4.13, 4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered, patient died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.18 (0.18, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e12.6 (12.0, 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not part of first course Tx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.35 (0.35, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.84 (4.73, 4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.63 (0.61, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.0 (1.88, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Radiation Therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Contraindicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.54 (0.51, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.52 (2.31, 2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.74 (0.71, 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.59 (1.50, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered, patient died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.37 (0.32, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.45 (3.49, 5.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not part of first course Tx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.71 (0.69, 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.69 (1.65, 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.71 (0.68, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.69 (1.60, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Immunotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Contraindicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.51 (0.45, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.78 (2.30, 3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.63 (0.55, 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.01 (1.66, 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered, patient died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.37 (0.32, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e4.43 (3.59, 5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not part of first course Tx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.49 (0.48, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.89 (2.78, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.82 (0.71, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.36 (1.12, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Hormone Therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Contraindicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.56 (0.52, 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.43 (2.18, 2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.54 (0.51, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.53 (2.34, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not administered, patient died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.27 (0.22, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e7.39 (5.58, 9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not part of first course Tx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.66 (0.63, 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.88 (1.76, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.54 (0.50, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e2.55 (2.32, 2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoexisting liver metastasis at diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.86 (0.61, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.26 (0.74, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e0.398\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.85 (0.81, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.28 (1.20, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"49.585798816568044%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.59171597633136%\"\u003e\n \u003cp\u003e0.63 (0.62, 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.94674556213018%\"\u003e\n \u003cp\u003e1.99 (1.95, 2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.875739644970414%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: NHL, non-Hodgkin lymphoma; NOS, not otherwise specified; Tx, treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Reference group for each respective explanatory variable in the multiple regression model. Whenever a reference category is not specified, the coefficients of the explanatory are compared to either blank values or the excluded group for binary variables (e.g., the reference group for variable \u0026ldquo;Sex\u0026rdquo; is \u0026ldquo;female\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003e\u0026dagger;The acceleration factor or AF in a log-logistic model is interpretable as multiplicative effects on the survival. This suggests that the median life expectancy of each corresponding group is AF times that of the reference group for the respective explanatory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026Dagger; The proportional odds or PO in a log-logistic model is interpretable as multiplicative effects on the hazard, likewise semiparametric Hazard Ratios. This suggests that the odds of death or hazard for each group is PO times the odds of the reference group for the respective explanatory in the model.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"brain metastases, cerebral metastases, brain metastasis, metastatic disease, topography, morphology, systemic malignancy","lastPublishedDoi":"10.21203/rs.3.rs-1559460/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1559460/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground:\u003c/em\u003e The primary site and histology of systemic malignancy are known predictors of progression to brain metastases(BM). We investigated the combinational interactions of ICD-O primary topography and morphology types on the survival of BM after adjusting for relevant clinical and demographic prognostic factors.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e The cohort included all adult patients with BM at diagnosis of an invasive malignancy in the National Cancer Database(2010-2018). The sample consisted of 180,150 entries out of 14,279,749 cancer patients screened. A survival analysis of the topography- and histology- specific time to death was performed. Multivariate Cox regression revealed violations of the proportional hazard assumption for multiple covariates. Parametric models using a log-logistic distribution best described the population survival pattern. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e The primary topography “prostate” and morphology “choriocarcinoma” provided the strongest survival benefit among ICD-O types, while BM from prostate demonstrated a 14-month median overall increase in survival probability. Favorable prognostics were BM from breast, bone/joints, and testis; also, the morphologies of carcinoid tumor, mature B-cell lymphoma, and papillary adenocarcinoma. Poor prognostics were BM from gastrointestinal(liver, biliary tree, pancreas, gallbladder) and gynecologic malignancies. All morphologies of spindle cell carcinoma, hemangiosarcoma, undifferentiated carcinoma, Ewing sarcoma, pseudosarcomatous carcinoma, renal cell carcinoma/sarcomatoid, signet ring cell carcinoma, spindle cell sarcoma, and squamous cell carcinoma/spindle cell were associated with poor survival.\u003c/p\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e This is the largest cohort providing an unbiased estimate of the adjusted ICD-O topography and morphology effect sizes. The results can be summarized as a booklet for prognostic classification of disease in patients with BM secondary to systemic malignancy.\u003c/p\u003e","manuscriptTitle":"Prognostics of systemic malignancy ICD-O topography and morphology types on brain metastases: an NCDB time-to-event cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-04-19 04:47:08","doi":"10.21203/rs.3.rs-1559460/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2022-04-18 19:56:58","doi":"10.21203/rs.3.rs-1559460/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"608535a2-8ccb-4a70-81d0-169a4b2b2c19","owner":[],"postedDate":"April 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-18T20:01:59+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-19 04:47:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1559460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1559460","identity":"rs-1559460","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}