Surveillance, Epidemiology, And End Results Data Research Articles

Sex differences in prognosis of primary bone cancer: a propensity score-matched study

Abstract Objectives Sex differences in survival in primary bone cancer have not been fully explored. Hence, this study was conducted to investigate the impact of sex on survival outcomes in patients with primary bone cancer. Methods The Surveillance, Epidemiology, and End Results (SEER)-17 database was used to identify patients with primary bone cancer. One-to-one propensity score matching (PSM) was employed to balance baseline characteristics. Kaplan-Meier curves and log-rank tests were used to evaluate differences in cancer-specific survival (CSS) and overall survival (OS) between sexes. Multivariate Cox regression analysis was performed to verify the independent effect of sex on survival, and sensitivity analysis was performed to determine the robustness of the results. Results A total of 8,791 patients were included, with 4,928 males (56.1 %) and 3,863 females (43.9 %), and a median follow-up time of 38 months. After PSM, 3,812 males and 3,812 females were included in the study, with balanced baseline characteristics between the groups. Post-PSM, females show significantly better CSS (HR=0.88, 95 % CI, 0.81–0.96, p=0.004) and OS (HR=0.87, 95 % CI, 0.81–0.94, p<0.001) compared to males. In multivariable Cox regression, the female sex was identified as an independent protective factor for both CSS (HR=0.86, 95 % CI, 0.79–0.94, p<0.001) and OS (HR=0.83, 95 % CI, 0.77–0.89, p<0.001). Conclusions Based on the analysis of SEER data with a large sample size, this study revealed that female patients with primary bone cancer have significantly better survival compared to males.

Recovery From COVID-19–Related Disruptions in Cancer Detection

The COVID-19 pandemic impacted the timely diagnosis of cancer, which persisted as the second leading cause of death in the US throughout the pandemic. To evaluate the disruption and potential recovery in cancer detection during the first (2020) and second (2021) years of the COVID-19 pandemic. This cross-sectional study involved an epidemiologic analysis of nationally representative, population-based cancer incidence data from the Surveillance, Epidemiology, and End Results (SEER) Program. Included patients were diagnosed with incident cancer from January 1, 2000, through December 31, 2021. The analysis was conducted in May 2024 using the April 2024 SEER data release, which includes incidence data through December 31, 2021. Diagnosis of cancer during the first 2 years of the COVID-19 pandemic (2020, 2021). Difference between the expected and observed cancer incidence in 2020 compared with 2021, with additional analyses by demographic subgroups (sex, race and ethnicity, and age group) and community (county-level) characteristics. The analysis included 15 831 912 patients diagnosed with invasive cancer between 2000 and 2021, including 759 810 patients in 2020 and 825 645 in 2021. The median age was 65 years (IQR, 56-75 years), and 51.0% were male. The percentage difference between the expected and observed cancer incidence was -8.6% (95% CI, -9.1% to -8.1%) in 2020, with no significant difference in 2021 (-0.2%; 95% CI, -0.7% to 0.4%). These translated to a cumulative (2020-2021) deficit in observed vs expected cases of -127 931 (95% CI, -139 206 to -116 655). Subgroup analyses revealed that incidence rates remained substantially depressed from expected rates into 2021 for patients living in the most rural counties (-4.9%; 95% CI, -6.7% to -3.1%). The cancer sites with the largest cumulative deficit in observed vs expected cases included lung and bronchus (-24 940 cases; 95% CI, -28 936 to -20 944 cases), prostate (-14 104 cases; 95% CI, -27 472 to -736 cases), and melanoma (-10 274 cases; 95% CI, -12 825 to -7724 cases). This cross-sectional study of nationally representative registry data found that cancer incidence recovered meaningfully in 2021 following substantial disruptions in 2020. However, incidence rates need to recover further to address the substantial number of patients with undiagnosed cancer during the pandemic.

Prognostic factors and surgical management in pediatric primary lung cancer: a retrospective cohort study using SEER data.

Primary lung cancer (LC) is extremely rare in pediatric patients, making diagnosis and management particularly challenging. Currently, there are no established guidelines for treating LC in this age group, and both prognosis and treatment experiences are scarcely studied. This study aims to evaluate prognostic factors and assess the survival benefits of surgical intervention in pediatric LC patients. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database spanning from 1988 to 2019, encompassing 337 children aged 0-19 years diagnosed with primary LC. Clinical characteristics, prognostic factors, and surgical approaches were elucidated. Prognostic markers for overall survival (OS) were evaluated through univariate and multivariate Cox proportional hazards regression models. Survival analysis between groups utilized Kaplan-Meier survival curves. The children indicated a median age of 15 years and the female-to-male ratio was close to 1:1. The most common pathological type was carcinoid tumor (31.45%). Most of the tumors were <5 cm in diameter (63.79%) or confined in situ (46.77%). The 5-year OS rate for the entire cohort was 77.9%, with pathologic classification, SEER stage, surgery, and tumor size identified as independent prognostic factors. Pulmonary/pleuropulmonary blastoma [hazards ratio (HR): 6.41; 95% confidence interval (CI): 1.69-24.35; P=0.006] or adenocarcinoma (HR: 8.82; 95% CI: 2.20-35.25; P=0.002), no surgery (HR: 2.05; 95% CI: 1.13-3.72; P=0.02), and tumor size ≥5 cm (HR: 2.87; 95% CI: 1.20-6.89; P=0.02) were associated with a worse prognosis in pediatric LC patients. In localized of SEER stage (HR: 0.15; 95% CI: 0.04-0.56; P=0.005) was associated with a better prognosis in pediatric LC patients. Common pathological types including carcinoid, pulmonary/pleuropulmonary blastoma, and mucoepidermoid carcinoma demonstrated the most favorable prognosis (P<0.001). Surgery did not significantly benefit patients with American Joint Committee on Cancer (AJCC) stage IV (HR: 0.83; 95% CI: 0.42-1.62; P=0.58) or distant-stage disease (HR: 0.59; 95% CI: 0.33-1.06; P=0.06). Conversely, children with regional lymph node metastasis (HR: 0.23; 95% CI: 0.06-0.88; P=0.02) or AJCC stage III-IV (HR: 0.40; 95% CI: 0.19-0.87; P=0.02) showed improved survival following lymph node dissection. Tumor size also influenced surgical decision-making, with smaller tumors (<5 cm) favoring surgical resection, including lobectomy (P<0.001) or local tumor resection (P=0.03), while larger tumors exhibited advantages with less specificity regarding surgical approach (P=0.15). This study identified pathologic classification, SEER stage, surgery, and tumor size as independent prognostic factors for pediatric LC. For children with advanced-stage LC, surgical intervention may not extend survival time. This study underscores the importance of tailoring therapeutic strategies based on histology, disease stage, and tumor size. These findings offer valuable insights into managing pediatric LC, enabling more informed clinical decisions and potentially enhancing patient outcomes.

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

Revisiting the survival paradox between stage IIB/C and IIIA colon cancer.

Patients with stage IIB/C (T4a-bN0) colon cancer often exhibit worse survival rates compared to those with stage IIIA (T1-2N1, T1N2a) colon cancer. This study re-evaluates the survival paradox using the latest Surveillance, Epidemiology, and End Results (SEER) data (released on April 17, 2024) to inform potential revisions to the staging criteria. Utilizing SEER data with 8th edition TNM staging criteria, 4692 colon cancer patients diagnosed between 2018 and 2021 were analyzed with chi-square test. Cox regression and Kaplan-Meier survival analysis were employed to assess factors associated with cancer-specific survival (CSS) and overall survival (OS). The 3-year CSS rates for stage IIB and IIC were 73.1% and 70.3%, respectively, whereas stage IIIA had a substantially higher CSS rate of 91% (P < 0.001). Similarly, the OS rates were 64.9% and 63.0% for stage IIB and IIC, respectively, compared to 83.1% for stage IIIA (P < 0.001). Multivariate analyses revealed stage IIIA patients had significantly lower risks of cancer-specific mortality (hazard ratio (HR) = 0.374, 95% CI: 0.296-0.473, P < 0.001) and overall mortality (HR = 0.575, 95% CI: 0.483-0.685, P < 0.001) compared to stage IIB patients. The upcoming 9th edition of the AJCC staging system should address this paradox by integrating advanced diagnostic markers and emphasizing the aggressive biology of T4 tumor, providing more accurate prognostic information and guiding more effective treatment strategies for colon cancer patients.

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for this cohort. Utilizing a retrospective design, this investigation leverages data extracted from the Surveillance, Epidemiology, and End Results (SEER) database, specifically the January 2024 subset, to conduct the analysis. The study cohort consists of 15,775 patients diagnosed with HCC as identified within the SEER database, spanning 2016 to 2020. In the construction of the diagnostic model, recursive feature elimination (RFE) is employed for variable selection, incorporating five critical predictors: age, tumor size, radiation therapy, T-stage, and serum alpha-fetoprotein (AFP) levels. These variables are the foundation for a stacking ensemble model, which is further elucidated through Shapley Additive Explanations (SHAP). Conversely, the prognostic model is crafted utilizing stepwise backward regression to select pertinent variables, including chemotherapy, radiation therapy, tumor size, and age. This model culminates in the development of a prognostic nomogram, underpinned by the Cox proportional hazards model. The outcome of the diagnostic model is the occurrence of DLNM in patients. The outcome of the prognosis model is determined by survival time and survival status. The integrated model developed based on stacking demonstrates good predictive performance and high interpretative variability and differentiation. The area under the curve (AUC) in the training set is 0.767, while the AUC in the validation set is 0.768. The nomogram, constructed using the Cox model, also demonstrates consistent and strong predictive capabilities. At the same time, we recognized elements that have a substantial impact on DLNM and the prognosis and extensively discussed their significance in the model and clinical practice. Our study identified key predictive factors for DLNM and elucidated significant prognostic indicators for HCC patients with DLNM. These findings provide clinicians with valuable tools to accurately identify high-risk individuals for DLNM and conduct more precise risk stratification for this patient subgroup, potentially improving management strategies and patient outcomes.

Development and validation of accelerated failure time model for cause-specific survival and prognostication of oral squamous cell carcinoma: SEER data analysis.

Oral Squamous Cell Carcinoma is the most prevalent malignancies affecting the oral cavity. Despite progress in studies and treatment options its outlook remains grim with survival prospects greatly affected by demographic and clinical factors. Precisely predicting survival rates and prognosis plays a role in making treatment choices for the best achievable overall health outcomes. To develop and validate an accelerated failure time model as a predictive model for cause-specific survival and prognosis of Oral Squamous Cell Carcinoma patients and compare its results to the traditional Cox proportional hazard model. We screened Oral cancer patients diagnosed with Squamous Cell Carcinoma from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2020. An accelerated failure time model using the Type I generalized half logistic distribution was used to determine independent prognostic factors affecting the survival time of patients with oral squamous carcinoma. In addition, accelerated factors were estimated to assess how some variables influence the survival times of the patients. We used the Akaike Information Criterion, Bayesian Information Criterion to evaluate the model fit, the area under the curve for discriminability, Concordance Index (C-index) and Root Mean Square Error and calibration curve for predictability, to compare the type I generalized half logistic survival model to other common classical survival models. All tests are conducted at a 0.05 level of significance. The accelerated failure time models demonstrated superior effectiveness in modeling (fit and predictive accuracy) the cause-specific survival (CSS) of oral squamous cell carcinoma compared to the Cox model. Among the accelerated failure time models considered, the Type I generalized half logistic distribution exhibited the most robust model fit, as evidenced by the lowest Akaike Information Criterion (AIC = 27370) and Bayesian Information Criterion (BIC = 27415) values. This outperformed other parametric models and the Cox Model (AIC = 47019, BIC = 47177). The TIGHLD displayed an AUC of 0.642 for discrimination, surpassing the Cox model (AUC = 0.544). In terms of predictive accuracy, the model achieved the highest concordance index (C-index = 0.780) and the lowest root mean square error (RMSE = 1.209), a notable performance over the Cox model (C-index = 0.336, RMSE = 6.482). All variables under consideration in this study demonstrated significance at the 0.05 level for CSS, except for race and the time span from diagnosis to treatment, in the TIGHLD AFT model. However, differences emerged regarding the significant variations in survival times among subgroups. Finally, the results derived from the model revealed that all significant variables except chemotherapy, all TNM stages and patients with Grade II and III tumor presentations contributed to the deceleration of time to cause-specific deaths. The accelerated failure time model provides a relatively accurate method to predict the prognosis of oral squamous cell carcinoma patients and is recommended over the Cox PH model for its superior predictive capabilities. This study also underscores the importance of using advanced statistical models to improve survival predictions and outcomes for cancer patients.

Incidence and Survival Outcomes of Gastrointestinal Stromal Tumors

The incidence of gastrointestinal stromal tumors (GISTs) increased after the implementation of GIST-specific histology coding in 2001, but updated data on trends and survival are lacking. To examine the evolving epidemiology of GISTs in major organ sites. This descriptive, population-based cohort study used nationally representative data from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) Program, including the SEER-22 and SEER-17 registries. Data were from evaluated patients aged 20 years or older with GISTs diagnosed between January 1, 2000, and December 31, 2019. Analyses were last updated on October 29, 2023. Organ site-specific trends in age-standardized incidence rates and annual percent changes (APCs) in rates were estimated by race and ethnicity and, when possible, by sex, age, and primary indicator. Multivariable Cox proportional hazards regression models were used to examine racial and ethnic differences in overall and GIST-specific survival by site. The SEER-22 and SEER-17 datasets contained 23 001 and 12 109 case patients with GISTs, respectively. Patients in the SEER-22 registry had a mean (SD) age of 64 (13) years and 51.3% were men. With regard to race and ethnicity, 9.7% of patients were Asian or Pacific Islander, 12.3% were Hispanic, 19.6% were non-Hispanic Black, and 57.7% were non-Hispanic White. Overall incidence rates of GISTs in the SEER-22 cohort increased substantially over time for all organ sites but the colon (APCs: esophagus, 7.3% [95% CI, 4.4% to 10.2%]; gastric, 5.1% [95% CI, 4.2% to 6.1%]; small intestine, 2.7% [95% CI, 1.8% to 3.7%]; colon, -0.2% [95% CI, -1.3% to 0.9%]; and rectum, 1.9% [95% CI, 0.1% to 3.8%]). There were similar increasing trends by age groups (<50 vs ≥50 years), sex, race and ethnicity, and primary indicator for gastric and small intestine GISTs. Increases were mainly restricted to localized stage disease. Patients in the SEER-17 cohort had a mean (SD) age of 64 (14) years and 51.9% were men. With regard to race and ethnicity, 13.3% of patients were Asian or Pacific Islander, 11.6% were Hispanic, 17.8% were non-Hispanic Black, and 56.6% were non-Hispanic White. Non-Hispanic Black individuals had higher overall mortality for esophageal (adjusted hazard ratio [HR], 6.4 [95% CI, 2.0 to 20.3]) and gastric (adjusted HR, 1.4 [95% CI, 1.2 to 1.5]) GISTs compared with non-Hispanic White individuals. Asian or Pacific Islander individuals also had higher overall mortality for esophageal GISTs (adjusted HR, 5.6 [95% CI, 1.5 to 20.2]). Results were similar for GIST-specific survival. In this cohort study using SEER data, the incidence of GISTs in major organ sites increased in the last 2 decades among several population groups. These findings suggest that additional studies are warranted to identify risk factors, because histologic reclassification and higher availability of endoscopy and imaging do not fully explain these unfavorable incidence trends. Prevention efforts are needed to reduce the substantial survival disparities among racial and ethnic minoritized populations.

The epidemiological and clinical characteristics of Langerhans cell sarcoma in the United States: A population study based on SEER data from 2000 to 2019.

Langerhans cell sarcoma (LCS) is a rare aggressive malignancy with a poor prognosis. Our knowledge about this condition is limited and mainly based on case reports, making it challenging to understand its epidemiology, clinical features, and patient outcomes. We conducted a retrospective study of LCS patients diagnosed between 2000 and 2019 using the Surveillance, Epidemiology, and End Results (SEER) database. The data were stratified based on age, race, stage, clinical pattern, and treatment method. Our study found that 57 LCS cases were reported in SEER registries between 2000 and 2019. Among these cases, most patients (50.9%) were over 60 years old and White (71.9%) with almost equal males to females ratio. About 45.6% of cases were localized while 47.4% were at distant stages. Of the patients, 50.9% underwent surgery, 45.6% received chemotherapy, and only 21.1% received radiotherapy. The overall survival rate for patients diagnosed with LCS in the United States is generally low with a 1-year overall rate of 63.8%. Certain factors can negatively impact prognosis, such as advanced stages of the disease, secondary tumors, or more than 1 tumor per patient. LCS is a rare disease with poor survival rates. Future research should incorporate global data for further statistically significant results. Moreover, investigating the molecular, genetic, and pathophysiological backgrounds of these tumors is crucial for developing targeted management strategies and improving prognosis.

The SEER Program's longstanding commitment to making cancer resources available.

The Surveillance, Epidemiology, and End Results (SEER) Program, an authoritative source of cancer statistics in the United States, has been funded by the National Cancer Institute (NCI) since 1973. The goals of SEER include measuring the cancer burden in the United States, making the results available, and supporting cancer research. These goals render products that serve as resources for different audiences, such as cancer registrars, researchers, and the public. Cancer registrars have access to a dedicated tab on the SEER website for questions, assistance with cancer coding, and training and technical resources such as SEER*Rx and SEER Data Management System (SEER*DMS). For researchers, SEER provides access to databases and software such as SEER*Stat and other linked databases such as SEER-Medicare that may offer answers to emerging issues in the field of cancer outcomes, cancer burden, health disparities, healthcare access, diagnosis, and prevention. The public can access cancer materials such as the SEER Cancer Stat Facts sheets, SEER*Explorer, and Did You Know? video series to learn more about cancer. SEER continues to update its resources with and ahead of legislation such as the Plain Writing Act and 21st Century Integrated Digital Experience Act (IDEA) to improve both clarity and user experience. Moving forward, SEER continues to promote awareness of SEER resources, remains accessible and understandable across all audiences, and standardizes how new resources are shared with these groups. The longstanding commitment to making cancer resources available is fundamental to the value of SEER, as evidenced by testimonials from members of various audience types.

Treatments and prognostic outcomes of combined hepatocellular-cholangiocarcinoma with distant metastasis: an analysis based on SEER data.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare liver cancer with a poor prognosis, often diagnosed at an advanced stage. The management of cHCC-CCA with distant metastasis remains challenging, and prognostic factors are not well-defined. This study aimed to investigate prognostic factors and treatment outcomes for cHCC-CCA patients with distant metastasis. Retrospective analysis was conducted using data from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database. Patients with distant metastasis [stage M1, according to the American Joint Committee on Cancer (AJCC) 7th edition] between January 2010 and December 2020 were included. Their characteristics, clinical profiles, and prognostic information were evaluated. Cox multifactorial survival analysis and Kaplan-Meier survival curves were used for statistical analysis. A total of 130 patients were included, with 78 (60%) receiving chemotherapy. Cox multivariate survival analysis revealed worse prognosis for Black individuals compared to White individuals (P<0.05). The median overall survival was 2 months for Black patients and 5 months for White patients. Chemotherapy significantly improved patient prognosis (P<0.05), while lung metastasis emerged as an independent risk factor (P<0.05). Kaplan-Meier survival curves confirmed the impact of lung metastasis and chemotherapy on overall survival. Patients with lung metastasis had lower survival rates (P<0.05), and those receiving chemotherapy had higher survival rates (P<0.05). Subgroup analysis based on age showed lower survival rates in patients aged 75 years or older compared to those below 75 years. Chemotherapy showed significant beneficial effects on the prognosis of patients below 75 years old, but no significant difference was observed in patients aged 75 years or above. Chemotherapy improves the prognosis of cHCC-CCA patients with distant metastasis, especially for those under 75 years old. Black race and lung metastasis are poor prognostic factors.

Characteristics and outcomes of secondary acute lymphoblastic leukemia (sALL) after multiple myeloma (MM): SEER data analysis in a single-center institution

BackgroundSecondary acute lymphoblastic leukemia (sALL) is rare in patients diagnosed with antecedent multiple myeloma (MM). This study aimed to elucidate the clinical features and outcomes of patients with sALL after MM. MethodsWe conducted this population-based study using the Surveillance, Epidemiology, and End Results (SEER) database and retrospectively reviewed patients with sALL following MM treatment at our institution. Cox regression analysis was performed to investigate the prognostic factors for survival in patients with sALL. ResultsWe identified 64,629 cases of MM (including 18 sALL from the SEER Plus 9 database, and three sALL from our institution). Younger patients with MM and those who received chemotherapy were at a higher risk of developing sALL. The novel agent era witnessed an increased incidence of sALL (post-novel agent era vs. pre-novel agent era: 0.31% [10/32,640] vs. 0.25% [8/31,989]) and shorter latency time (post-novel agent era vs. pre-novel agent era [median]: 51.5 vs. 74.5 months, P = 0.516), though the difference was not significant. The median age at sALL onset was 65 (range: 47–78) years. Significant cytopenia and absence of BCR/ABL fusion genes were common features in this patient population. The treatment of sALL is complicated by old age and poor performance status. The median survival of patients with sALL is 18 months, whereas those who received chemotherapy had significantly prolonged survival. ConclusionsPatients with sALL combined with an antecedent MM, especially those with long-term exposure to immunomodulatory agents such as thalidomide or lenalidomide, should be cautiously evaluated and managed with a comprehensive approach.

Constructing a prognostic model for colorectal cancer with synchronous liver metastases after preoperative chemotherapy: a study based on SEER and an external validation cohort.

The combination of preoperative chemotherapy and surgical treatment has been shown to significantly enhance the prognosis of colorectal cancer with liver metastases (CRLM) patients. Nevertheless, as a result of variations in clinicopathological parameters, the prognosis of this particular group of patients differs considerably. This study aimed to develop and evaluate Cox proportional risk regression model and competing risk regression model using two patient cohorts. The goal was to provide a more precise and personalized prognostic evaluation system. We collected information on individuals who had a pathological diagnosis of colorectal cancer between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) Database. We obtained data from patients who underwent pathological diagnosis of colorectal cancer and got comprehensive therapy at the hospital between January 1, 2010, and June 1, 2022. The SEER data collected after screening according to the inclusion and exclusion criteria were separated into two cohorts: a training cohort (training cohort) and an internal validation cohort (internal validation cohort), using a random 1:1 split. Subgroup Kaplan-Meier (K-M) survival analyses were conducted on each of the three groups. The data that received following screening from the hospital were designated as the external validation cohort. The subsequent variables were chosen for additional examination: age, gender, marital status, race, tumor site, pretreatment carcinoembryonic antigen level, tumor size, T stage, N stage, pathological grade, number of tumor deposits, perineural invasion, number of regional lymph nodes examined, and number of positive regional lymph nodes. The primary endpoint was median overall survival (mOS). In the training cohort, we conducted univariate Cox regression analysis and utilized a stepwise regression approach, employing the Akaike information criterion (AIC) to select variables and create Cox proportional risk regression models. We evaluated the accuracy of the model using calibration curve, receiver operating characteristic curve (ROC), and area under curve (AUC). The effectiveness of the models was assessed using decision curve analysis (DCA). To evaluate the non-cancer-related outcomes, we analyzed variables that had significant impacts using subgroup cumulative incidence function (CIF) and Gray's test. These analyses were used to create competing risk regression models. Nomograms of the two models were constructed separately and prognostic predictions were made for the same patients in SEER database. This study comprised a total of 735 individuals. The mOS of the training cohort, internal validation cohort, and QDU cohort was 55.00months (95%CI 46.97-63.03), 48.00months (95%CI 40.65-55.35), and 68.00months (95%CI 54.91-81.08), respectively. The multivariate Cox regression analysis revealed that age, N stage, presence of perineural infiltration, number of tumor deposits and number of positive regional lymph nodes were identified as independent prognostic risk variables (p < 0.05). In comparison to the conventional TNM staging model, the Cox proportional risk regression model exhibited a higher C-index. After controlling for competing risk events, age, N stage, presence of perineural infiltration, number of tumor deposits, number of regional lymph nodes examined, and number of positive regional lymph nodes were independent predictors of the risk of cancer-specific mortality (p < 0.05). We have developed a prognostic model to predict the survival of patients with synchronous CRLM who undergo preoperative chemotherapy and surgery. This model has been tested internally and externally, confirming its accuracy and reliability.

Incidence trends and factors affecting survival in mucinous adenocarcinoma of the lung: Insights from SEER data.

e20090 Background: Lung cancer is the second most common cancer in terms of incidence and the leading cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for 80 to 85% of cases. Adenocarcinoma, squamous cell carcinoma, and large-cell carcinoma are the main subtypes of NSCLC. Mucinous adenocarcinoma is the least common subtype of lung adenocarcinoma with poor prognosis. Our study aims to identify demographics, incidence trends, and factors impacting overall survival (OS) in mucinous adenocarcinoma of the lung. Methods: We conducted a retrospective cohort analysis utilizing the Surveillance, Epidemiology, and End Results (SEER) database to investigate patients diagnosed with mucinous adenocarcinoma of the lung between the years 2000 and 2020. Demographics, treatment data were characterized using descriptive statistics. The Cox proportional regression analysis was used to identify the factors that impact the OS. Results: Between 2000-2020, a total of 7,953 patients were diagnosed with mucinous adenocarcinoma of the lung. The highest incidence of mucinous adenocarcinoma was in older adults, accounting for 67% of all cases. Females had a higher incidence rate at 53.6%. Caucasians comprised the majority of patients at 83.1%, followed by blacks at 8.8%. The majority of patients, 89.3% resided in urban areas, and 43.6% had annual incomes exceeding $75k. Most of the patients were married at 58.2%. The incidence trends revealed a significant increase from 2000 to 2020. The incidence rates were: 15.76% (2000 -2004), 16.37% (2005 -2009), 31.08% (2010 - 2014), and 36.79% (2015-2020). However, the survival rates improved over time (See table). Among the demographic factors: adults, females, Asian/ Natives/ &amp; other races combined, urban residence, higher annual income (&gt; $75k), and married status had better OS outcomes than their counterparts. Among disease stages, localized disease had the best, while regional disease had a better OS than distant disease. In terms of treatment, surgery was associated with better OS; interestingly, receipt of radiation or chemotherapy was not associated with better OS. The HRs of all these factors are shown in Table. Conclusions: Our study, the largest to date on mucinous adenocarcinoma of the lung, demonstrated a rising incidence yet with improved OS rates from 2000-2020. Our findings shed light on the demographics and other factors influencing OS in this rare subtype of NSCLC. [Table: see text]

Demographics and survival outcomes of stage IV male breast cancer: A retrospective analysis of SEER database.

11142 Background: Male Breast Cancer (MBC) is a rare malignancy accounting for &lt;1% of all breast cancer cases in the United States. It is often lately diagnosed due to lack of awareness leading to substantial morbidity and mortality. Previous studies revealed higher risk of death among males compared to females across all stages, but little known about the impact of Hormone Receptor (HR) and Human Epidermal growth factor Receptor 2(HER 2) statuses on prognosis and survival among MBC patients. So, we aim to study the survival outcomes of stage IV MBC patients using SEER (Surveillance, Epidemiology, and End Results) data. Methods: A retrospective analysis was conducted using a SEER database and identified stage IV MBC cases (Any T, Any N, M1) diagnosed between 2010-2015. The selection of these years is based on the reporting of HER2 status on SEER database starting from 2010 and to ensure a current period and a minimum of 5 years of follow-up. Patient demographics, tumor HR/HER 2 status and survival data were extracted and analyzed. Subgroup analyses were performed to show the correlation between HR/HER 2 status and survival. Results: The study included 144 patients. 59.7%(86/144) were above 60 years of age. Majority were Caucasians up to 68.1%(98/144) followed by African Americans 18.7% (27/144), Hispanics 6.9%(10/144) and Asians/Pacific Islanders 6.2%(9/144). Among the subtypes, the most common was HR+/HER2- consisting of 69.4%(100/144) followed by HR+/HER2+ 19.4%(28/144), HR-/HER2- 9.7%(14/144), HR-/HER2+ 1.4%(2/144). The median overall survival varied among different subtypes of breast cancer and was noted to be 31.5 months, 25 months, 4.5 months and 10 months among HR+/HER2-, HR+/HER2+, HR-/HER2- and HR-/HER2+ respectively (Table). In the comparison between HR+/HER2+ and HR-/HER2-, the risk of death differed significantly, as indicated by a hazard ratio of 0.26 (95% CI 0.12-0.56, p = 0.001), highlighting a poor survival of HR-/HER2-. Conclusions: This retrospective analysis provides valuable insights into the demographics and survival outcomes of stage IV MBC patients. Triple negative (HR-/HER2-) subtype continues to have poor outcomes in males as well, emphasizing the need to explore novel treatment strategies to improve outcomes for this unique patient population. Although, treatment regimens for MBC are extrapolated from women, the disparity in survival across all subtypes requires the need to get more men on breast cancer clinical trials when feasible. [Table: see text]

Racial/ethnic distribution of participants in NRG oncology clinical trials: 2014-2023.

e13706 Background: External validity and applicability of clinical research relies upon characteristics of trial participants matching those of the respective disease groups in the general population. Understanding the racial/ethnic distribution of patients enrolled in recent US-based, federally sponsored Phase II and III clinical trials could inform strategies to further align characteristics of trial participants with those of disease group populations. As such, we report the racial/ethnic distribution of participants in NRG Oncology studies during 2014-2023 and compare the distribution to that from the Surveillance, Epidemiology, and End Results (SEER) program data during 2014-2020 (most recent data available). Methods: Proportions of self-reported race/ethnicity were summarized for the 33,370 trial participants enrolled since Mar 1, 2014 (NRG Oncology inception) to therapeutic, symptoms management, and other interventional trials. SEER proportions were used to benchmark NRG trials for representativeness of disease burden in the population. Results: Among NRG trial participants, 71.5% were non-Hispanic (NH) White, 9.7% NH Black, 4.9% Hispanic, and 7.8% N-H Asian (Table). Examining distributions within selected cancer sites relative to cancer proportional incidence in 2014-2020 SEER data, NRG accrual to prostate, lung, cervix, and breast cancer trials is commensurate with observed disease burden for NH Blacks and modestly lower for Asians. For other sites shown, representation was 2 to 5 percentage points lower than SEER frequencies for Blacks and Asians. For all sites, accrual of individuals of Hispanic background was lower than that reported in SEER. As the NRG trials do not reflect sampling across stages and other features of a given cancer in the population, further analysis to be presented will consist of more detailed breakdowns of trial participant disease-specific cancer stage and tumor subtype and comparison to relevant observations from the 2014-2020 SEER database. Conclusions: This high-level comparison of the racial/ethnic distribution of NRG trial participants provides supportive information for population representative enrollment in several cancer sites, but also identifies opportunities to improve enrollment diversity to represent more closely the respective affected population. Areas of success identified merit further investigation to understand if there are best practices which may be applied more broadly towards expanding access to federally funded clinical trials. [Table: see text]

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Enhancing clinical decision-making: A novel nomogram for stratifying cancer-specific survival in middle-aged individuals with follicular thyroid carcinoma utilizing SEER data

BackgroundThyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. MethodsWe collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. ResultsA total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805–0.927), for the validation cohort it was 0.944 (95 % CI: 0.903–0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997–1.001). Calibration curves and AUC suggest that the model has good accuracy. ConclusionsWe developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma.

To find the factors impacting overall survival (OS) prognosis in patients with endometrioid endometrial carcinoma (EEC) and adenocarcinoma and to establish a nomogram model to validate the 2023 International Federation of Obstetrics and Gynecology (FIGO) staging system for endometrial cancer. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) training cohort. An independent validation cohort was obtained from the First Affiliated Hospital of Anhui Medical University between 2008 and 2023. Cox regression analysis identified independent prognostic factors for OS in EEC and adenocarcinoma patients. A nomogram predicting OS was developed and validated utilizing the C-index, calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The relationship between the tumor grade and prognosis of EEC and adenocarcinoma was quantified using net reclassification improvement (NRI), propensity score matching (PSM), and Kaplan-Meier curves. Cox regression analysis identified age, race, marital status, tumor grade, tumor stage, tumor size, and chemotherapy as independent prognostic factors for OS. A nomogram for predicting OS was developed based on these factors. The C-indexes for the OS nomogram was 0.743 and 0.720 for the SEER training set and external validation set, respectively. The area under the ROC (AUC) for the OS nomogram was 0.755, 0.757, and 0.741 for the SEER data subsets and 0.844, 0.719, and 0.743 for the external validation subsets. Calibration plots showed high concordance between the nomogram-predicted and observed OS. DCA also demonstrated the clinical utility of the OS nomogram. NRI, PSM, and survival analyses revealed that tumor grade was the most important histopathological factor for EEC and adenocarcinoma prognosis. Seven independent prognostic variables for the OS of patients with EEC and adenocarcinoma were identified. The established OS nomogram has good predictive ability and clinical utility and validates the 2023 endometrial cancer FIGO staging system.

Survival Outcomes in Thyroid Cancer Patients with Co-Occurring Breast Cancer: Evidence of Mortality Risk Attenuation

BackgroundPrevious studies have reported a strong correlation between breast cancer (BC) and thyroid cancer (TC) incidence. However, the clinical and oncological impact of these associations are not yet fully understood. Here, we aimed to explore the differences in clinicopathological characteristics between TC patients with and without BC, and the effect of a history of positive BC on TC survival. MethodsWe retrospectively compared the clinical characteristics and survival rates of patients with TC alone and those with TC and BC in a primary cohort at our institution and in a second cohort using the Surveillance, Epidemiology, and End Results (SEER) database. ResultsIn our institutional cohort, survival rates were similar between patients with TC alone and those with TC-associated BC. However, using SEER data, we found that BC had a protective effect on TC patients and was associated with reduced TC mortality rates (hazard ratio [HR] = 0.72, 95% confidence interval [CI] 0.57 to 0.92; P = .026). After stratifying the TC patients according to co-occurring BC subtypes, we observed that higher survival rates were restricted to patients with coexisting luminal A BC (P = .015), which exhibit positive hormone receptors and do not express HER-2. ConclusionThese findings suggest that hormone pathways may play a role in the co-occurrence of thyroid and breast cancers. Patients with TC coexisting with luminal A BC have higher survival rates. However, further studies on the mechanisms underlying the association between BC and TC are warranted.