Evolving patterns in systemic treatment utilization and survival among older patients with advanced cutaneous melanoma.

INTRODUCTION: In the last decade, melanoma treatment has improved significantly. However, data on population-level treatment utilization and survival trends among older patients is limited. This study aimed to analyze trends in systemic anticancer therapy (Rx), including the uptake of immune checkpoint inhibitors (ICIs), in conjunction with trends in cause-specific survival among older patients (66+) diagnosed with advanced melanoma (2008-2019). METHODS: We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare Condensed Resource to assess any Rx utilization among patients first diagnosed with advanced melanoma in 2008-2010, 2011-2014, and 2015-2019, stratified by stage, and type of first-line Rx among patients receiving Rx. The SEER dataset was used to evaluate trends in cause-specific survival by year of diagnosis. RESULTS: Rx utilization (any type) almost doubled, from 28.6% (2008-2010) to 55.4% (2015-2019) for stage 3 melanoma, and from 35.5% to 68.0% for stage 4 melanoma. In 2008-2010, the standard first-line treatment was cytokines/cytotoxic chemotherapy/other. By 2015-2019, only 5.1% (stage 3) and <3.6% (stage 4) of patients receiving Rx received these agents, as ICIs emerged as the dominant treatment. Both 1-year and 5-year cause-specific survival significantly improved since 2010 for stage 4 and since 2013 for stage 3. CONCLUSIONS: This study shows a significant rise in Rx utilization and a rapid transition from cytokines/cytotoxic chemotherapy to ICIs, reflecting a rapid uptake of highly effective treatment in a previously challenging disease with limited options before 2011. The documented survival improvement aligns with the adoption of these novel treatments, underscoring their significant impact on real-world patient outcomes.

Establishment of a nomogram model for predicting distant metastasis in pancreatic ductal adenocarcinoma: a comparative analysis of different lymph node staging systems based on the SEER database.

The purpose of this study was to compare the predictive value of different lymph node staging systems and to develop an optimal prognostic nomogram for predicting distant metastasis in pancreatic ductal adenocarcinoma (PDAC). Our study involved 6364 patients selected from the Surveillance, Epidemiology, and End Results (SEER) database and 126 patients from China. Independent risk factors for distant metastasis were screened by univariate and multivariate logistic regression analyses, and a model-based comparison of different lymph node staging systems was conducted. Furthermore, we developed a nomogram for predicting distant metastasis using the optimal performance lymph node staging system. The lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), age, primary site, grade, tumor size, American Joint Committee on Cancer (AJCC) 7th Edition T stage, and radiotherapy recipient status were significant predictors of distant metastasis in PDAC patients. The model with the LODDS was a better fit than the model with the LNR. We developed a nomogram model based on LODDS and six clinical parameters. The area under the curve (AUC) and concordance index (C-index) of 0.753 indicated that this model satisfied the discrimination criteria. Kaplan-Meier curves indicate a significant difference in OS among patients with different metastasis risks. LODDS seems to have a superior ability to predict distant metastasis in PDAC patients compared with the AJCC 8th Edition N stage, PLN and LNR staging systems. Moreover, we developed a nomogram model for predicting distant metastasis. Clinicians can use the model to detect patients at high risk of distant metastasis and to make further clinical decisions.

High cardiovascular mortality risk among older merkel cell carcinoma patients.

OBJECTIVE: Previous research has primarily focused on the incidence and mortality rates of Merkel cell carcinoma (MCC), neglecting the examination of cardiovascular mortality (CVM) risk among survivors, particularly older patients. This study aims to assess the risk of CVM in older individuals diagnosed with MCC. METHODS: Data pertaining to older MCC patients were obtained from the Surveillance, Epidemiology, and End Results database (SEER). CVM risk was measured using standardized mortality ratio (SMR) and cumulative mortality. Multivariate Fine-Gray's competing risk model was utilized to evaluate the risk factors contributing to CVM. RESULTS: Among the study population of 2,899 MCC patients, 465 (16.0%) experienced CVM during the follow-up period. With the prolongation of the follow-up duration, the cumulative mortality rate for CVM reached 27.36%, indicating that cardiovascular disease (CVD) became the second most common cause of death. MCC patients exhibited a higher CVM risk compared to the general population (SMR: 1.69; 95% CI: 1.54-1.86, p < 0.05). Notably, the SMR for other diseases of arteries, arterioles, and capillaries displayed the most significant elevation (SMR: 2.69; 95% CI: 1.16-5.29, p < 0.05). Furthermore, age at diagnosis and disease stage were identified as primary risk factors for CVM, whereas undergoing chemotherapy or radiation demonstrated a protective effect. CONCLUSION: This study emphasizes the significance of CVM as a competing cause of death in older individuals with MCC. MCC patients face a heightened risk of CVM compared to the general population. It is crucial to prioritize cardiovascular health starting from the time of diagnosis and implement personalized CVD monitoring and supportive interventions for MCC patients at high risk. These measures are essential for enhancing survival outcomes.

Toward real-time reporting of cancer incidence: methodology, pilot study, and SEER Program implementation.

BACKGROUND: A lag time between cancer case diagnosis and incidence reporting impedes the ability to monitor the impact of recent events on cancer incidence. Currently, the data submission standard is 22 months after a diagnosis year ends, and the reporting standard is 27.5 months after a diagnosis year ends. This paper presents the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program's efforts to minimize the lag and achieve "real-time" reporting, operationalized as submission within 2 months from the end of a diagnosis year. METHODS: Technology for rapidly creating a consolidated tumor case (CTC) from electronic pathology (e-path) reports is described. Statistical methods are extended to adjust for biases in incidence rates due to reporting delays for the most recent diagnosis years. RESULTS: A registry pilot study demonstrated that real-time submissions can approximate rates obtained from 22-month submissions after adjusting for reporting delays. A plan to be implemented across the SEER Program rapidly ascertains unstructured e-path reports and uses machine learning algorithms to translate the reports into the core data items that comprise a CTC for incidence reporting. Across the program, cases were submitted 2 months after the end of the calendar year. Registries with the most promising baseline values and a willingness to modify registry operations have joined a program to become certified as real-time reporting. CONCLUSION: Advances in electronic reporting, natural language processing, registry operations, and statistical methodology, energized by the SEER Program's mobilization and coordination of these efforts, will make real-time reporting an achievable goal.

Optimal treatment strategies for hepatoid adenocarcinoma of the lung: insights from a comprehensive analysis.

BACKGROUND: Hepatoid adenocarcinoma of the lung (HAL) is a distinctly uncommon subtype of lung adenocarcinoma (LAC), characterized by hepatoid features and an alarmingly low 5-year survival rate of approximately 8%. The scarcity of information on this condition has contributed to the absence of standardized treatment protocols, and the molecular underpinnings of its pathogenesis remain largely unexplored. To bridge these gaps, this study compiled data from 191 primary HAL patients to delineate treatment patterns, prognostic factors, and potential pathogenic mechanisms. METHODS: This study was divided into two cohorts: cohort 1, comprising 110 patients extracted from the Surveillance, Epidemiology, and End Results (SEER) database, and cohort 2, consisting of 70 patients identified through a comprehensive literature review via the PubMed, Web of Science, and Scopus databases, in addition to 11 patients from Tongji Hospital. The Cox proportional hazards regression model was employed to identify independent prognostic factors. Kaplan-Meier survival curves were generated to assess the impact of treatment modalities centered around surgery and chemotherapy. Moreover, this study evaluated the efficacy of first-line treatment regimens and conducted Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses on identified mutated genes. RESULTS: The demographic and clinical profile of HAL patients typically comprises older individuals who are smokers, with a predisposition for diagnosis at advanced disease stages, culminating in a high mortality rate. Key prognostic indicators identified included disease stage, chemotherapy and surgical interventions. The study suggests a treatment strategy that advocates chemotherapy for patients with stage IV HAL and surgery for those with non-stage IV disease. The combination of paclitaxel and platinum-based chemotherapy emerged as an efficacious first-line treatment, with the integration of immunotherapy and targeted therapies showing potential benefits. Genetic analysis underscored similarities between HAL and LAC, particularly highlighting aberrant kinase activity (serine, threonine, and tyrosine) and the activation of PI3K-Akt and MAPK signaling pathways as contributing factors to HAL pathogenesis. CONCLUSION: Despite its relatively rare occurrence, this study underscores the significance of treatment strategies and concludes probable prognostic factors. Due to limited reports, a deeper understanding of the molecular mechanisms driving tumorigenesis and progression in HAL is needed.

History of the Surveillance, Epidemiology, and End Results (SEER) Program.

The Surveillance, Epidemiology, and End Results (SEER) Program established in 1973 was the first laboratory for experimenting with new methods for cancer data collection and translating the data into population-based cancer statistics. The SEER Program staff have been instrumental in the development of the International Classification of Disease-Oncology and successfully implemented the routine collection of anatomic and prognostic cancer stage at diagnosis. Currently the program consists of 21 central registries that generate cancer statistics covering more than 48% of the US population and an additional 10 research support registries contributing to certain research projects, such as the National Childhood Cancer Registry. In parallel with the geographical expansion, the program built an architecture of methods and tools for population-based cancer statistics, with SEER*Explorer as the most recent online tool for descriptive statistics. In addition, SEER releases annual updates for a comprehensive data product line, which includes SEER*Stat databases with an annual caseload of more than 800 000 incident cases. Furthermore, the program developed a full suite of analytical applications for population-based cancer statistics that include Joinpoint (regression-based trend analysis), DevCan (risk of diagnosis and death), CanSurv (survival models), and ComPrev and PrejPrev (cancer prevalence), among others. The future of the SEER Program is closely aligned to the overall goals of the "war on cancer." The program aims to release longitudinal treatment data coupled with a comprehensive genomic characterization of cancers with a declared goal of decreasing the cancer burden and disparities across a wide spectrum of diseases and communities.

Landscape analysis of environmental data sources for linkage with SEER cancer patients database.

One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

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.

The SEER Program's evolution: supporting clinically meaningful population-level research.

Although the Surveillance, Epidemiology, and End Results (SEER) Program has maintained high standards of quality and completeness, the traditional data captured through population-based cancer surveillance are no longer sufficient to understand the impact of cancer and its outcomes. Therefore, in recent years, the SEER Program has expanded the population it covers and enhanced the types of data that are being collected. Traditionally, surveillance systems collected data characterizing the patient and their cancer at the time of diagnosis, as well as limited information on the initial course of therapy. SEER performs active follow-up on cancer patients from diagnosis until death, ascertaining critical information on mortality and survival over time. With the growth of precision oncology and rapid development and dissemination of new diagnostics and treatments, the limited data that registries have traditionally captured around the time of diagnosis-although useful for characterizing the cancer-are insufficient for understanding why similar patients may have different outcomes. The molecular composition of the tumor and genetic factors such as BRCA status affect the patient's treatment response and outcomes. Capturing and stratifying by these critical risk factors are essential if we are to understand differences in outcomes among patients who may be demographically similar, have the same cancer, be diagnosed at the same stage, and receive the same treatment. In addition to the tumor characteristics, it is essential to understand all the therapies that a patient receives over time, not only for the initial treatment period but also if the cancer recurs or progresses. Capturing this subsequent therapy is critical not only for research but also to help patients understand their risk at the time of therapeutic decision making. This article serves as an introduction and foundation for a JNCI Monograph with specific articles focusing on innovative new methods and processes implemented or under development for the SEER Program. The following sections describe the need to evaluate the SEER Program and provide a summary or introduction of those key enhancements that have been or are in the process of being implemented for SEER.

Real-world lessons: combining cancer registry and retail pharmacy data for oral cancer drugs.

BACKGROUND: Recent cancer care advances have introduced new oral therapies, and yet population registries lack detailed treatment data, hampering investigations into therapy uptake, adherence, and outcomes. OBJECTIVE: This study aimed to assess the representativeness and completeness of linking Surveillance, Epidemiology, and End Results (SEER) cancer registry data with data from two major retail pharmacy chains, collectively covering a large segment of the US market. METHODS: A deterministic data linkage between 11 SEER cancer registries and retail pharmacy data (excluding mail order fills) was conducted for individuals diagnosed with selected cancers from 2013 to 2017, with follow-up through 2019. Descriptive characteristics of the linked and unlinked populations were examined. In a selected subcohort of older women (aged ≥65) with first and only primary breast cancer who had Medicare Part D claims for tamoxifen, we further validated the linkage using Medicare Part D event data as the reference standard. RESULTS: Among 758 068 eligible individuals, only 6.4% were linked to CVS/Walgreens data; the linkage percentage varied by age, sex, race, ethnicity, registry, and cancer type. Within the subcohort of 5963 older women with breast cancer and a claim for tamoxifen in Part D data, 25% were identified as tamoxifen users in retail pharmacy data. Out of these 1490 women, 749 (50.3%) had complete longitudinal tamoxifen dispensing information from retail pharmacy data. CONCLUSION: Retail pharmacy data show promise in identifying oral anticancer treatments, enhancing SEER registry efforts, but they require further validation. We propose an evaluation framework, sharing insights and potential use cases for this resource.

Survival characteristics of Wilms Tumor, a reference developed from a longitudinal cohort study.

BACKGROUND: Wilms tumor (WT) survival has been affected by the evolution in clinical and biological prognostic factors. Significant differences in survival rates indicate the need for further efforts to reduce these disparities. This study aims to evaluate the clinicopathological data impact on survival among patients after Wilm's diagnosis. METHODS: The study utilized the SEERStat Database to identify Wilms tumor patients, applying SEERStat software version 8.3.9.2 for data extraction. Selection criteria involved specific codes based on the International Classification of Diseases for Oncology (ICDO-3), excluding cases with unknown SEER stage, incomplete survival data, unknown size, or lymph node status. Statistical analyses, including Kaplan-Meier estimates and Cox regression models, were conducted using R software version 3.5. Standardized mortality ratios (SMR) were computed with SEER*Stat software, and relative and conditional survival analyses were performed to evaluate long-term survival outcomes. RESULTS: Of 2273 patients diagnosed with Wilms tumor, (1219 patients, 53.6% were females with an average age group of 3-8 years (50.2%). The overall mean survival after five years of diagnosis was 93.6% (2.6-94.7), and the overall mean survival rate was 92.5% (91.3-93.8) after ten years of diagnosis. Renal cancers were identified as the leading cause of death (77.3%), followed by nonrenal cancers (11%) and noncancer causes (11%). Additionally, robust relative survival rates of 98.10%, 92.80%, and 91.3% at one, five, and ten years, respectively, were observed, with corresponding five-year conditional survival rates indicating an increasing likelihood of survival with each additional year post-diagnosis. Univariate Cox regression identified significant prognostic factors: superior CSS for patients below 3 years (cHR 0.48) and poorer CSS for those older than 15 years (cHR 2.72), distant spread (cHR 10.24), regional spread (cHR 3.09), and unknown stage (cHR 4.97). In the multivariate model, age was not a significant predictor, but distant spread (aHR 9.22), regional spread (aHR 2.84), and unknown stage (aHR 4.98) were associated with worse CSS compared to localized tumors. CONCLUSION: This study delving into WT survival dynamics reveals a multifaceted landscape influenced by clinicopathological variables. This comprehensive understanding emphasizes the imperative for ongoing research and personalized interventions to refine survival rates and address nuanced challenges across age, stage, and tumor spread in WT patients.

A Nomogram for Predicting Cancer-Specific Survival in Young Patients With Advanced Lung Cancer Based on Competing Risk Model.

BACKGROUND: Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer-specific survival (CSS) in young patients with advanced stage. METHODS: Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18-45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine-Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve were applied for validation. RESULTS: The proportion of cancer-specific death (CSD) in young patients was higher than that in old patients with early-stage lung cancer (p < 0.001), while there was no difference in the advanced stage (p = 0.999). Through univariate and multivariate analysis, 10 variables were identified as independent prognostic factors for CSS. The AUC of the 1-, 3-, and 5-year prediction of CSS was 0.688, 0.706, and 0.791 in the training cohort and 0.747, 0.752, and 0.719 in the validation cohort. The calibration curves demonstrated great accuracy. The C-index of the competing risk model was 0.692 (95% CI: 0.636-0.747) in the young patient cohort. CONCLUSION: Young lung cancer is a distinct entity with a different spectrum of competing risk events. The construction of our nomogram can provide new insights into the management of young patients with lung cancer.

Evaluation of neoadjuvant chemotherapy for clinical T1 triple-negative breast cancer.

The role of neoadjuvant chemotherapy and its benefits in patients with triple-negative breast cancer (TNBC) and small tumors are unclear. This study aims to compare survival differences between clinical T1 TNBC receiving neoadjuvant chemotherapy (NAC) and adjuvant chemotherapy (AC). Data for patients with clinical T1 TNBC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized according to whether they received chemotherapy before or after surgery. Propensity Score Matching (PSM) was used to minimize the influence of confounding factors. OS and BCSS were compared between the two treatment sequences using Kaplan-Meier and univariate and multivariable Cox proportional hazards regression analyses. The study included 6249 women with T1 TNBC. In multivariate analysis, compared with that in the AC group, the hazard ratio for death in the NAC group was 1.54 (95% confidence interval 1.26-1.89, p < 0.001). NAC offers no additional benefits in any age group or T, N subgroups. Our findings suggest that NAC does not provide additional benefit to patients with clinical T1 TNBC, even in the presence of lymph node metastasis, or T1c.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

Prognostic Impact of Neoadjuvant Chemotherapy in Localized or Locoregionally Advanced Gallbladder Cancer: A Population-Based and Propensity Score Matched SEER Analysis.

BACKGROUND: The effect of neoadjuvant chemotherapy (NACT) in gallbladder cancer (GBC) patients remains controversial. The aim of this study was to assess the impact of NACT on overall survival (OS) and cancer specific survival (CSS) in patients with localized or locoregionally advanced GBC, and to explore possible protective predictors for prognosis. METHODS: Data for patients with localized or locoregionally advanced GBC (i.e., categories cTx-cT4, cN0-2, and cM0) from 2004 to 2020 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients in the NACT and non-NACT groups were propensity score matched (PSM) 1:3, and the Kaplan-Meier method and log-rank test were performed to analyze the impact of NACT on OS and CSS. Univariable and multivariable Cox regression models were applied to identify the possible prognostic factors. Subgroup analysis was conducted to identify patients who would benefit from NACT. RESULTS: Of the 2676 cases included, 78 NACT and 234 non-NACT patients remained after PSM. In localized or locoregionally advanced GBC patients, the median OS of the NACT and non-NACT was 31 and 16 months (log-rank P < 0.01), and the median CSS of NACT and non-NACT was 32 and 17 months (log-rank P < 0.01), respectively. Longer median OS (31 vs 17 months, log-rank P < 0.01) and CSS (32 vs 20 months, log-rank P < 0.01) was associated with NACT compared with surgery alone. Multivariable Cox regression analysis showed that NACT, stage, and surgery type were prognostic factors for OS and CSS in GBC patients. Subgroup analysis revealed that the survival hazard ratios (HRs) of NACT vs non-NACT for localized or locoregionally advanced GBC patients were significant in most subgroups. CONCLUSIONS: NACT may provide therapeutic benefits for localized or locoregionally advanced GBC patients, especially for those with advanced stage, node-positive, poorly differentiated or undifferentiated disease. NACT combined with radical surgery was associated with a survival advantage. Therefore, NACT combined with surgery may provide a better treatment option for resectable GBC patients.

Establishment of Prognostic Nomogram for Male Breast Cancer Patients: A Surveillance, Epidemiology and End Results Database Analysis.

BACKGROUND: Male breast cancer (MBC) represents a rare subtype of breast cancer, with limited prognostic factor studies available. The purpose of this research was to develop a unique nomogram for predicting MBC patient overall survival (OS) and breast cancer-specific survival (BCSS). METHODS: From 2010 to 2020, clinical characteristics of male breast cancer patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Following univariate and multivariate analyses, nomograms for OS and BCSS were created. Kaplan-Meier plots were further generated to illustrate the relationship between independent risk variables and survival. The nomogram's ability to discriminate was measured by employing the area under a time-dependent receiver operating characteristic curve (AUC) and calibration curves. Additionally, when the nomogram was used to direct clinical practice, we also used decision curve analysis (DCA) to evaluate the clinical usefulness and net clinical benefits. RESULTS: A total of 2143 patients were included in this research. Univariate and multivariate analysis showed that age, grade, surgery, chemotherapy status, brain metastasis status, subtype, marital status, race, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with OS. Lung metastasis, age, marital status, grade, surgery, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with BCSS. By comprising these variables, a predictive nomogram was constructed in the SEER cohort. Then, it could be validated well in the validation cohort by receiver operating characteristics (ROCs) curve and calibration plot. Furthermore, the nomogram demonstrated better decision curve analysis (DCA) results, indicating the ability to forecast survival probability with greater accuracy. CONCLUSION: We created and validated a unique nomogram that can assist clinicians in identifying MBC patients at high risk and forecasting their OS/BCSS.

Causes of death analysis and the prognostic model construction in neuroendocrine carcinoma of the cervix: A SEER-based study.

PURPOSE: Neuroendocrine carcinoma of the cervix (NECC) is rare but results in poor prognosis. The causes of death (CODs) in NECC patients are rarely reported. Our study aimed to explore the distributions of death causes of NECC patients compared with squamous cell carcinoma (SCC) and adenocarcinoma (ADC) and to develop a validated survival prediction model. METHODS: Patients diagnosed with NECC, SCC, or ADC were identified from the Surveillance, Epidemiology, and End Results Program database from 1975 to 2019. We analyzed the standardized mortality ratio (SMR) to determine each cause of death for each survival time category. The Kaplan-Meier method was used for survival analysis. Univariate and multivariate Cox regression analyses were used to establish a nomogram model. RESULTS: A total of 358 NECC patients were included in this study, and 270 (75.4%) died during the follow-up period. Patients with NECC had 5.55 times (95% CI, 4.53-6.79, p < 0.0001) higher risk of death compared with patients with SCC and 10.38 times (95% CI, 8.28-13.01, p < 0.0001) higher compared with ADC. Cervical cancer is the main cause of death in NECC. As the diagnosis time increased, the risk of death from all causes and cervix cancer gradually decreased. While after at least 10 years of follow-up time, the highest and most dramatical SMR values were observed for metastasis (SMR, 138.81; 95% CI, 37.82-355.40; p < 0.05) and other cancers as the reason for death has an over 7-fold higher SMR (SMR: 7.07; 95% CI: 2.60-15.40, p < 0.05) more than 5 years after the cancer diagnosis. Race, FIGO stage, and surgery were independent risk factors for the overall survival (OS) of NECC patients. For the predictive nomogram, the C-index was 0.711 (95% CI: 0.697-0.725) and was corrected to 0.709 (95% CI: 0.680, 0.737) by bootstrap 1000 resampling validation. CONCLUSION: Compared with SCC and ADC, NECC patients have an elevated risk of mortality due to cervical cancer and metastasis. We successfully constructed a prognostic nomogram for patients with NECC. Based on refractoriness and high mortality of NECC, targeted treatment strategies and follow-up plans should be further developed according to the risk of death and distribution characteristics of CODs.

The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC) and Other Real-World Databases.

BACKGROUND: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed. OBJECTIVES: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases. METHODS: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death. RESULTS: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%). CONCLUSION: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.