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.

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

BackgroundReduced overall survival has been observed in patients with left-sided versus right-sided breast cancer due to cardiac toxicity after radiotherapy. However, the effect of laterality and primary tumor site on breast cancer-specific mortality (BCSM) remains unclear.Patients and MethodsWe analyzed data from 305,443 women ages 20- to 79-years-old diagnosed with breast cancer between 1990 and 2009. The data were obtained from the population-based Surveillance, Epidemiology, and End Results (SEER) program of the U.S. National Cancer Institute. The survival outcomes with regard to laterality and primary tumor site were compared using univariate and multivariate (Cox proportional hazards regression model) methods.ResultsIn the multivariate analysis, BCSM was affected by the primary tumor site (P

IntroductionTriple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis.MethodsWe included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis.ResultsIn the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately.ConclusionThese nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

ObjectiveTriple negative breast cancer (TNBC) is a more aggressive subtype resistant to conventional treatments with a poorer prognosis. This study was to update the status of TNBC and the temporal changes of its incidence rate in the US.MethodsWomen diagnosed with breast cancer during 2011–2019 were obtained from the National Program of Cancer Registries (NPCR) and Surveillance, Epidemiology and End Results (SEER) Program SEER*Stat Database which covers the entire population of the US. The TNBC incidence and its temporal trends by race, age, region (state) and disease stage were determined during the period.ResultsA total of 238,848 (or 8.8%) TNBC women were diagnosed during the study period. TNBC occurred disproportionally higher in women of Non-Hispanic Black, younger ages, with cancer at a distant stage or poorly/undifferentiated. The age adjusted incidence rate (AAIR) for TNBC in all races decreased from 14.8 per 100,000 in 2011 to 14.0 in 2019 (annual percentage change (APC) = −0.6, P = 0.024). Incidence rates of TNBC significantly decreased with APCs of −0.8 in Non-Hispanic White women, −1.3 in West and −0.7 in Northeastern regions. Women with TNBC at the age of 35–49, 50–59, and 60–69 years, and the disease at the regional stage displayed significantly decreased trends. Among state levels, Mississippi (20.6) and Louisiana (18.9) had the highest, while Utah (9.1) and Montana (9.6) had the lowest AAIRs in 2019. New Hampshire and Indiana had significant and highest decreases, while Louisiana and Arkansas had significant and largest increases in AAIR. In individual races, TNBC displayed disparities in temporal trends among age groups, regions and disease stages. Surprisingly, Non-Hispanic White and Hispanic TNBC women (0–34 years), and Non-Hispanic Black women (≥70 years) during the entire period, as well as Asian or Pacific Islander women in the South region had increased trends between 2011 and 2017.ConclusionOur study demonstrates an overall decreased trend of TNBC incidence in the past decade. Its incidence displayed disparities among races, age groups, regions and disease stages. Special attention is needed for a heavy burden in Non-Hispanic Black and increased trends in certain groups.

Data support a paper of this title:

A Geotemporospatial and Causal Inference Epidemiological Exploration of Substance and Cannabinoid Exposure as Drivers of Rising US Pediatric Cancer Rates

Data represent a compilation of various data inputs from numerous sources including the National Cancer Institute SEER*Stat National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database: NPCR and SEER Incidence – U.S. Cancer Statistics Public Use Research Database, 2019 submission (2001-2017), United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Released June 2020. Available at www.cdc.gov/cancer/public-use program; the National survey of Drug Use and Health conducted by the Substance Abuse and Mental Health Services Administration; and the US Census bureau.

Data also include inverse probability weights for cannabis exposure.

Data also include their geospatial linkage network constructed for all US states which makes Alaska and Hawaii spatially connected to the contiguous USA.

Data also include the R script used to conduct and prepare the analysis.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

BackgroundImmunotherapy research for esophageal cancer is progressing rapidly, particularly for locally advanced unresectable cases. Despite these advances, the prognosis remains poor, and traditional staging systems like AJCC inadequately predict outcomes. This study aims to develop and validate a nomogram to predict cancer-specific survival (CSS) in these patients.MethodsClinicopathological and survival data for patients diagnosed between 2010 and 2021 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into a training cohort (70%) and a validation cohort (30%). Prognostic factors were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. A nomogram was constructed based on the training cohort and evaluated using the concordance index (C-index), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plots, and area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to validate the prognostic factors.ResultsThe study included 4,258 patients, and LASSO-Cox regression identified 10 prognostic factors: age, marital status, tumor location, tumor size, pathological grade, T stage, American Joint Committee on Cancer (AJCC) stage, SEER stage, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.660 (training set) and 0.653 (validation set), and 1-, 3-, and 5-year AUC values exceeded 0.65. Calibration curves showed a good fit, and decision curve analysis (DCA), IDI, and NRI indicated that the nomogram outperformed traditional AJCC staging in predicting prognosis.ConclusionsWe developed and validated an effective nomogram model for predicting CSS in patients with locally advanced unresectable esophageal cancer. This model demonstrated significantly superior predictive performance compared to the traditional AJCC staging system. Future research should focus on integrating emerging biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), into prognostic models to enhance their predictive accuracy and adapt to the evolving landscape of immunotherapy in esophageal cancer management.

This dataset was produced as part of the Surveillance, Epidemiology, and End Results (SEER) Program to monitor the incidence of cancer and cancer survival rates in the United States, thus carrying out the mandates of the National Cancer Act. The SEER Program had several objectives: to estimate the annual cancer incidence in the United States, to examine trends in cancer patient survival, to identify cancer etiologic factors, and to monitor trends in the incidence of cancer in selected geographic areas with respect to demographic and social characteristics. Data collection began in 1973, and by 1977 had a population base of 11 geographic areas in the United States and Puerto Rico. SEER variables include patient demographic information (age, sex, race, birthplace, marital status, census tract) and information on cancer, which was gathered from hospitals, clinics, private laboratories, private practitioners, nursing/convalescent homes, autopsies, and death certificates. The medical data cover histologic type, anatomic site, laterality, multiplicity within primary site at first diagnosis, diagnostic procedures, diagnostic confirmation, sequence of the tumor, extent of the disease, treatment of the lesion, and outcome.

The data for this article: predicting diagnosis and survival of bone metastasis in breast cancer using machine learning: a SEER-based study.

The Seer central air conditioner is a high-efficiency cooling system known for its exceptional performance and energy-saving features. It has a high SEER rating, utilizes environmentally friendly refrigerant, and features advanced filtration systems for cleaner indoor air quality. With its quiet operation and durability, it is an excellent choice for homeowners looking to enhance their indoor comfort while reducing energy consumption and utility costs.

PurposePrevious research has shown that bladder cancer has one of the highest incidences of developing a second primary malignancy. So, we designed this study to further examine this risk in light of race and histology.Patients and methodsUsing the surveillance, epidemiology, and end results (SEER) 18 registry, we retrospectively screened patients who had been diagnosed with bladder cancer between 2000 and 2018. We then tracked these survivors until a second primary cancer diagnosis, the conclusion of the trial, or their deaths. In addition to doing a competing risk analysis, we derived standardized incidence ratios (SIRs) and incidence rate ratios (IRRs) for SPMs by race and histology.ResultsA total of 162,335 patients with bladder cancer were included, and during follow-ups, a second primary cancer diagnosis was made in 31,746 of these patients. When the data were stratified by race, SIRs and IRRs for SPMs showed a significant difference: Asian/Pacific Islanders (APIs) had a more pronounced increase in SPMs (SIR: 2.15; p 0.05) than White and Black individuals who had an SIRs of 1.69 and 1.94, respectively; p 0.05. In terms of histology, the epithelial type was associated with an increase in SPMs across all three races, but more so in APIs (IRR: 3.51; 95% CI: 2.11–5.85; p 0.001).ConclusionWe found that race had an impact on both the type and risk of SPMs. Additionally, the likelihood of an SPM increases with the length of time between the two malignancies and the stage of the index malignancy.

BackgroundGastrointestinal neuroendocrine tumor (GI-net) is a rare heterogeneous tumor, and there is a lack of models to predict its prognosis. Our study aims to develop and validate two new nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of GI-net patients and investigate their application value.MethodsSEER*Stat 8.4.4 software was used to download clinicopathological information of GI-net patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training group (n=3007) and an internal-validation group (n=1289) at a 7:3 ratio. Patients from the Fourth Hospital of Hebei Medical University were enrolled in this study to form the external-validation group (n=86). Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. X-tile was used to divide GI-net patients into high-, medium-, and low-risk groups. Kaplan–Meier (KM) curves and log-rank tests were used to compare survival differences among the three groups.ResultsSeven variables (age, site, size, grade, M stage, surgery, and chemotherapy) were selected to establish the nomogram for OS, and 6 variables (age, size, grade, M stage, surgery, and chemotherapy) were selected for CSS. The C indices (0.785, 0.813, and 0.936 in the training, internal-validation, and external-validation groups for OS; 0.888, 0.893, and 0.930 for CSS, respectively) and AUCs (≥0.7) indicated that the nomograms had satisfactory discriminative ability. Calibration curve analysis and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. KM curves indicated that each of the two nomograms clearly differentiated the high-, medium-, and low-risk groups. In addition, two online risk calculators were developed to predict the OS and CSS of these patients visually.ConclusionsOur nomograms may play an important role in predicting 3- and 5-year OS and CSS for GI-net patients. Risk stratification systems and online risk calculators can be utilized in clinical practice to help doctors create personalized treatment plans.

PurposeThe aim of this study was to investigate the survival benefits of postoperative radiotherapy (PORT) in patients with resectable esophageal cancer (EC) after neoadjuvant therapy in the Immunotherapy era.MethodsThe study was designed as a retrospective cohort study, which included a total of 733 patients with EC from the SEER database and a single-center cohort. We used propensity score matching (PSM) to equilibrate patient characteristics. The investigation incorporated Kaplan-Meier survival analysis and the Cox proportional risk regression model to assess outcomes.ResultsPORT did not significantly improve survival in the overall cohort, with a median overall survival of 38 months (p=0.56) in the SEER cohort and 39 months (p=0.75) in the Chinese cohort. However, in the immunotherapy subgroup, the Chinese cohort demonstrated that immunotherapy combined with PORT significantly improved survival (p=0.044).Multivariate Cox regression analysis demonstrated that patients aged 50-59 years (HR=5.93, 95% CI: 1.67-21.06) and those aged ≥70 years (HR=10.96, 95% CI:3.04-39.56) had increased survival risks compared to patients aged

Additional file 1: Appendix Table 1. Cox regression coefficients of the two models of the SEER training set. AJCC: the American Joint Committee on Cancer; CI: confidence interval; HR: Hazard Ratio; Ln_surg: Lymph node dissection; SEER: the Surveillance Epidemiology, and End Results database.

NA- not assessed/availableWT: wild typeTumor characteristics and prognosis.

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

BackgroundAs lung squamous cell carcinoma (LUSC) patients are at increased risk of developing a second primary cancer, this complicates the patient’s condition and thus makes prognostic assessment more difficult, posing a significant prognostic challenge for clinicians. Our goal was to assess the prognosis of LUSC patients with a second primary tumor, and provide insights into appropriate therapy and monitoring strategies.MethodsData was obtained for LUSC patients from the Surveillance, Epidemiology, and End Results (SEER) database. The LUSC patients were divided into three groups (LS-SPM, OT-LUSC and LUSC-only). Univariate and stratified analyses were performed for the baseline and clinical characteristics of the participants. Multiple regression and Kaplan-Meier survival analyses were also performed, followed by a final life table analysis.ResultsIn our sample of 101,626 patients, the HR for OS in the LS-SPM group was 0.40 in univariate analysis. Kaplan-Meier survival curves showed that LS-SPM patients had considerably longer lifespans compared to the other groups. The LS-SPM patients had median and mean survival times of 64 months and 89.11 months. Unadjusted and adjusted multiple regression analyses showed that LS-SPM patients had a superior survival compared to LUSC-only and OT-LUSC groups.ConclusionLS-SPM patients have a good prognosis with aggressive therapy and immune monitoring. The present study offers novel insights into the pathophysiological causes and treatments for LS-SPM.

NA- not assessed/availablelcSSc- limited cutaneous systemic sclerosisdcSSc- diffuse cutaneous systemic sclerosisSSc/PPM- Systemic sclerosis and polymyositis overlapACA- anticentromere antibodySCL-70- anti-SCL 70 antibodyRNA Pol- anti-RNA polymerase III antibody* SCL-70 not assessedDemographics, smoking history and SSc subtype and serology.

BackgroundWith the development of surgical techniques and advances in systemic treatments, the survival time of esophageal cancer survivors has increased; however, the chance of developing a second primary malignancy (SPM) has also increased. These patients’ prognosis and treatment plans remain inconclusive.ObjectivesWe aimed to evaluate and predict the survival of patients with esophageal cancer with second primary tumors, to provide insights and the latest data on whether to pursue more aggressive treatment.Materials and MethodsWe selected esophageal cancer cases from the latest available data from the SEER database on April 15, 2021. We performed life table analysis, Kaplan–Meier analysis, and univariate and multivariate Cox proportional hazards analysis to assess the patient data. We conducted multiple Cox regression equation analyses under multiple covariate adjustment models, and performed a stratified analysis of multiple Cox regression equation analysis based on different covariates. To describe our study population more simply and clearly, we defined the group of patients with esophageal cancer combined with a second primary malignant tumor (the first of two or more primaries) as the EC-SPM group.ResultsOur analysis of 73,456 patients with esophageal cancer found the median survival time of the EC-SPM group was 47.00 months (95% confidence interval (CI), 43.87–50.13), and the mean survival time was 74.67 months (95% CI, 72.12–77.22). Kaplan–Meier curves of different esophageal cancer survivors showed that the survival of the EC-SPM group was significantly better than that of the other groups (p