SEER Limited-Use cancer incidence data with associated population data. Geographic areas available are county and SEER registry. The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute collects and distributes high quality, comprehensive cancer data from a number of population-based cancer registries. Data include patient demographics, primary tumor site, morphology, stage at diagnosis, first course of treatment, and follow-up for vital status. The SEER Program is the only comprehensive source of population-based information in the United States that includes stage of cancer at the time of diagnosis and survival rates within each stage.

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

The State Cancer Profiles (SCP) web site provides statistics to help guide and prioritize cancer control activities at the state and local levels. SCP is a collaborative effort using local and national level cancer data from the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and National Cancer Institute's Surveillance, Epidemiology and End Results Registries (SEER). SCP address select types of cancer and select behavioral risk factors for which there are evidence-based control interventions. The site provides incidence, mortality and prevalence comparison tables as well as interactive graphs and maps and support data. The graphs and maps provide visual support for deciding where to focus cancer control efforts.

Flat file of United States County-level cancer incidence rates obtained from: https://www.statecancerprofiles.cancer.gov/incidencerates/ All data housed on that website are extracts from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program with rates computed using SEER*Stat as documented in the About section of the above website.

Cancer incidence is rising among adolescents (“teens”). The causes of the increase are unknown but studying incidence patterns and trends may produce insights into etiology. Using data from the US National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program we described trends of cancer incidence among teens (15–19 year olds). We reviewed and summarized incidence patterns for histologic cancer groups and the most frequently diagnosed sites of cancer among teens during 2008–2012 reported by the SEER Cancer Statistics Review. We calculated annual incidence rates for the years 1975–2012 and used linear regression analysis to evaluate trends and calculate rates of change. Incidence for all sites combined increased annually by 0.67% for males and 0.62% for females during the period 1975 through 2012 –resulting in more than a 25% increase over 38 years. The biggest annual incidence increases occurred in non-Hodgkin lymphoma (NHL) (2.16% females; 1.38% males), thyroid cancer (2.12% females; 1.59% males), acute myeloid leukemia (AML) (1.73% females) and testicular cancer (1.55% males). Incidence rates for most histologic groups and sites showed steady long term increases over the 38 years of data. Despite improvements in survival, rising incidence trends mean growing numbers of young adults are undergoing painful and costly cancer treatments. A concerted research program is vital to investigate causes of steadily rising teen cancer rates.

ObjectiveProstate cancer (PC) is the second leading cause of cancer death in men in the United States after lung cancer in global incidence. Elderly male patients over 65 years old account for more than 60% of PC patients, and the impact of surgical treatment on the prognosis of PC patients is controversial. Moreover, there are currently no predictive models that can predict the prognosis of elderly PC patients undergoing surgical treatment. Therefore, we aimed to construct a new nomogram to predict cancer-specific survival (CSS) in elderly PC patients undergoing surgical treatment.MethodsData for surgically treated PC patients aged 65 years and older were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for elderly PC patients undergoing surgical treatment. A nomogram of elderly PC patients undergoing surgical treatment was developed based on the multivariate Cox regression model. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the predictive model. Decision curve analysis (DCA) was used to examine the potential clinical value of this model.ResultsA total of 44,975 elderly PC patients undergoing surgery in 2010–2018 were randomly assigned to the training set (N = 31705) and validation set (N = 13270). the training set was used for nomogram development and the validation set was used for internal validation. Univariate and multivariate Cox regression model analysis showed that age, marriage, TNM stage, surgical style, chemotherapy, radiotherapy, Gleason score(GS), and prostate-specific antigen(PSA) were independent risk factors for CSS in elderly PC patients undergoing surgical treatment. The C index of the training set and validation indices are 0.911(95%CI: 0.899–0.923) and 0.913(95%CI: 0.893–0.933), respectively, indicating that the nomogram has a good discrimination ability. The AUC and the calibration curves also show good accuracy and discriminability.ConclusionsTo our knowledge, our nomogram is the first predictive model for elderly PC patients undergoing surgical treatment, filling the gap in current predictive models for this PC patient population. Our data comes from the SEER database, which is trustworthy and reliable. Moreover, our model has been internally validated in the validation set using the C-index,AUC and the and the calibration curve, showed that the model have good accuracy and reliability, which can help clinicians and patients make better clinical decision-making. Moreover, the DCA results show that our nomogram has a better potential clinical application value than the TNM staging system.

Additional file 1: Figure S1. Kaplan-Meier survival curve in the validation set.

BackgroundThis study aims to predict the lymphatic metastasis in Ewing's sarcoma (ES) patients by nomogram. The risk of lymphatic metastasis in patients with ES was predicted by the built model, which provided guidance for the clinical diagnosis and treatment planning.MethodsA total of 929 patients diagnosed with ES were enrolled from the year of 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The nomogram was established to determine predictive factors of lymphatic metastasis according to univariate and multivariate logistic regression analysis. The validation of the model performed using multicenter data (n = 51). Receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the prediction accuracy of the nomogram. Decision curve analysis (DCA) was implemented to illustrate the practicability of the nomogram clinical application. Based on the nomogram, we established a web calculator to visualize the risk of lymphatic metastases. We further plotted Kaplan-Meier overall survival (OS) curves to compare the survival time of patients with and without lymphatic metastasis.ResultsIn this study, the nomogram was established based on six significant factors (survival time, race, T stage, M stage, surgery, and lung metastasis), which were identified for lymphatic metastasis in ES patients. The model showed significant diagnostic accuracy with the value of the area under the curve (AUC) was 0.743 (95%CI: 0.714–0.771) for SEER internal validation and 0.763 (95%CI: 0.623–0.871) for multicenter data external validation. The calibration plot and DCA indicated that the model had vital clinical application value.ConclusionIn this study, we constructed and developed a nomogram with risk factors to predict lymphatic metastasis in ES patients and validated accuracy of itself. We found T stage (Tx OR = 2.540, 95%CI = 1.433–4.503, P < 0.01), M stage (M1, OR = 2.061, 95%CI = 1.189–3.573, P < 0.05) and survival time (OR = 0.982, 95%CI = 0.972–0.992, P < 0.001) were important independent factors for lymphatic metastasis in ES patients. Furthermore, survival time in patients with lymphatic metastasis or unclear situation (P < 0.0001) was significantly lower. It can help clinicians make better decisions to provide more accurate prognosis and treatment for ES patients.

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

Users can download the data set and static graphs, tables and charts regarding cancers in the United States. Background The United States Cancer Statistics is web-based report created by the Centers for Disease Control and Prevention, in partnership with the National Cancer Institute (NCI) and the North American Association of Central Cancer Registries (NAACCR). The site contains cancer incidence and cancer mortality data. Specific information includes: the top ten cancers, state vs. national comparisons, selected cancers, childhood cancer, cancers grouped by state/ region, cancers gr ouped by race/ ethnicity and brain cancers by tumor type. User Functionality Users can view static graphs, tables and charts, which can be downloaded. Within childhood cancer, users can view by year and by cancer type and age group or by cancer type and racial/ ethnic group. Otherwise, users can view data by female, male or male and female. Users may also download the entire data sets directly. Data Notes The data sources for the cancer incidence data are the CD C's National Program for Cancer Registries (NPCR) and NCI's Surveillance, Epidemiology and End Result (SEER). CDC's National Vital Statistics System (NVSS) collects the data on cancer mortality. Data is available for each year between 1999 and 2007 or for 2003- 2007 combined. The site does not specify when new data becomes available.

Annual percent change and average annual percent change in age-standardized cancer incidence rates since 1984 to the most recent diagnosis year. The table includes a selection of commonly diagnosed invasive cancers, as well as in situ bladder cancer. Cases are defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3) from 1992 to the most recent data year and on the International Classification of Diseases, ninth revision (ICD-9) from 1984 to 1991.

ObjectivesWe aimed to determine trends in incidence and survival in patients with gastrointestinal neuroendocrine tumors (GI-NETs) from 1977 to 2016, and then analyze the potential risk factors including sex, age, race, grade, Socioeconomic status (SES), site, and stage.MethodsData were obtained from Surveillance, Epidemiology, and End Results Program (SEER) database. Kaplan-Meier survival analysis, relative survival rates (RSRs), and Cox proportional risk regression model were used to evaluate the relationship between these factors and prognosis.ResultsCompared with other sites, the small intestine and rectum have the highest incidence, and the appendix and rectum had the highest survival rate. The incidence was higher in males than in females, and the survival rate in males was close to females. Blacks had a higher incidence rate than whites, but similar survival rates. Incidence and survival rates were lower for G3&4 than for G1 and G2. Age, stage, and grade are risk factors.ConclusionsThis study described changes in the incidence and survival rates of GI-NETs from 1977 to 2016 and performed risk factor analyses related to GI-NETs.

The number of new cases, age-standardized rates and average age at diagnosis of cancers diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

A complete list of live websites using the Seers Cookie Consent Banner Privacy Policy technology, compiled through global website indexing conducted by WebTechSurvey.

SEER cancer incidence per 100,000 and percent contribution, 2008–2012a.

BackgroundIt remains controversial whether radiotherapy (RT) improves survival in patients with stage IIB/III PDAC. A growing number of studies have found that patients’ age at diagnosis and tumor site not only affect prognosis, but also may lead to different treatment responses. Therefore, the purpose of this study was to verify whether the survival effect of radiotherapy in patients with stage IIB/III PDAC varies across age and tumor site groups.MethodsThe target population was selected from PDAC patients undergone surgery in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. This study performed the Pearson’s chi-square test, Cox regression analysis, Kaplan-Meier (K-M) method, and focused on propensity frequency matching analysis.ResultsNeither neoadjuvant radiotherapy (nRT) nor adjuvant radiotherapy (aRT) patient group had probably improved survival among early-onset patients. For middle-aged patients, nRT seemed to fail to extend overall survival (OS), while aRT might improve the OS. Plus, both nRT and aRT were associated with improved survival in elderly patients. The aRT might be related with survival benefits in patients with pancreatic head cancer, while nRT was not. And RT in patients with PDAC at other sites did not appear to provide a survival benefit.ConclusionCarefully selected data from the SEER database suggested that age and tumor location may be the reference factors to guide the selection of RT for patients with stage IIB/III PDAC. These findings are likely to contribute to the development of personalized treatment for patients with stage IIB/III PDAC.

BackgroundModerately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure.MethodsWe collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used nine machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of six lymph node indicators on DM incidence and patient prognosis.ResultsWe collected data from 5,377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC = 0.919). T stage, primary site, chemotherapy, and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site, and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting the 1-, 3-, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS.ConclusionThe random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.

Background: The role of surgery and surgery type in de novo stage IV breast cancer (BC) is unclear.Methods: We carried out a retrospective cohort study that included the data of 4,108 individuals with de novo stage IV BC abstracted from SEER (Surveillance, Epidemiology, and End Results) data resource from 2010 to 2015. The patients were stratified into the non-surgery group, breast-conserving (BCS) surgery group, and mastectomy group. Inverse probability propensity score weighting (IPTW) was then used to balance clinicopathologic factors. Overall survival (OS), as well as the breast cancer-specific survival (BCSS), was assessed in the three groups using Kaplan–Meier analysis and COX model. Subgroups were stratified by metastatic sites for analysis.Results: Of the 4,108 patients, 48.5% received surgery and were stratified into the BCS group (574 cases) and mastectomy group (1,419 cases). After IPTW balance demographic and clinicopathologic factors, BCS and mastectomy groups had better OS (BCS group: HR, 0.61; 95% CI: 0.49–0.75; mastectomy group: HR, 0.7; 95% CI: 0.63–0.79) and BCSS (BCS group: HR, 0.6; 95% CI, 0.47–0.75; mastectomy group: HR, 0.71; 95% CI, 0.63–0.81) than the non-therapy group. Subgroup analyses revealed that BCS, rather than mastectomy, was linked to better OS (HR, 0.66; 95% CI: 0.48–0.91) and BCSS (HR, 0.63; 95% CI: 0.45–0.89) for patients with bone-only metastasis. For patients with viscera metastasis or bone+viscera metastases, BCS achieved similar OS (viscera metastasis: HR, 1.05; 95% CI: 0.74–1.48; bone+viscera metastases: HR, 1.01; 95% CI: 0.64–1.61) and BCSS (viscera metastasis: HR, 0.94; 95% CI: 0.64–1.38; bone+viscera metastases: HR, 1.06; 95% CI: 0.66–1.73) in contrast with mastectomy.Conclusions: Local surgery for patients with distant metastasis (DS) exhibited a remarkable survival advantage in contrast with non-operative management. BCS may have more survival benefits for patients with de novo stage IV BC with bone-only metastasis than other metastatic sites. Decisions on de novo stage IV BC primary surgery should be tailored to the metastatic pattern.

Incidence rate trend slopes.

Total incidence rate change 1975–2012 and estimated change in diagnoses nationally.