End Results Database Research Articles

Dynamic estimates of survival in patients with follicular thyroid cancer: a retrospective cohort study.

Few studies have been conducted on the dynamic survival rates of follicular thyroid cancer (FTC). This study aimed to ascertain how the survival probability of patients with FTC changes over time. In this retrospective analysis, 10,617 patients diagnosed with FTC between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database were included. Actuarial disease-specific survival (DSS) was estimated using the Kaplan-Meier method, and the log-rank test was used for comparisons. The annual hazard of mortality was determined using the hazard function, and the conditional survival (CS) was calculated using the life table method. A total of 459 (4.3%) patients died of FTC, and the 5-year and 10-year DSS rates were 96.6 ± 0.2% and 94.6 ± 0.3%, respectively. There was a statistically significant difference in the DSS rate between patients with different SEER combined summary stages (P < 0.001). The annual hazard curve for cancer mortality in the entire study cohort displayed a steep downward trend with a slight peak at 2.5 years after diagnosis, followed by a gradual decline. Patients with distant metastases exhibited a higher mortality hazard curve and more notable declining trend. CS demonstrated an upward trend across the entire study population, with the most pronounced trend in patients with distant metastases. Prognosis improved over time in a stage-dependent manner in patients with FTC after diagnosis. The most significant improvement was observed in the patients with distant metastases. Notably, dynamic survival estimations, such as death hazard and conditional survival analysis, provide more precise survival projections than traditional survival analysis for FTC survivors.

The prognostic impact of tumor deposits in colorectal cancer: More than just N1c.

The identification of tumor deposits (TD) currently plays a limited role in staging for colorectal cancer (CRC) aside from N1c lymph node designation. The objective of this study was to determine the prognostic impact, beyond American Joint Committee on Cancer N1c designation, of TDs among patients with primary CRC. Patients who had resected stage I-III primary CRC diagnosed between 2010 and 2019 were identified from the National Cancer Institute's Surveillance, Epidemiology, and End Results database. Cancer-specific survival (CSS) stratified by TD status and lymph node (N) status was calculated using the Kaplan-Meier method and multivariable Cox proportional hazards regression analyses. In total, 147,783 patients with primary CRC were identified. TDs were present in 15,444 patients (10.5%). The presence of TDs was significantly associated with adverse tumor characteristics, including advanced pathologic stage, nodal status, and metastasis status. The presence of TDs was associated with worse CSS (hazard ratio [HR], 3.12; 95% confidence interval [CI], 3.02-3.22), as it was for each given N category (e.g., N2a and TD-negative [HR, 2.50; 95% CI, 2.37-2.64] vs. N2a and TD-positive [HR, 3.75; 95% CI, 3.49-4.03]). The presence of multiple TDs was also associated with decreased CSS for each given N category compared with a single TD (e.g. N2a with one TD [HR, 3.09; 95% CI, 2.65-3.61] vs. N2a with two or more TDs [HR, 4.32; 95% CI, 3.87-4.82]). TDs were identified as an independent predictor of a worse outcome in patients with CRC. The presence of TDs confers distinctly different CSS and provides important prognostic information among patients with CRC and warrants further investigation as a unique variable in future iterations of CRC staging.

Survival outcomes of neoadjuvant versus adjuvant therapy in patients with T1c, node‐negative, human epidermal growth factor receptor 2–positive breast cancer: A Surveillance, Epidemiology, and End Results population‐based study

AbstractBackgroundPersistent debates exist regarding the superiority of neoadjuvant therapy (NAT) over adjuvant therapy (AT) for patients with T1c, node‐negative, human epidermal growth factor receptor 2–positive (HER2+) breast cancer, and relevant guidelines for these patients are lacking.MethodsData on patients with T1cN0M0‐stage HER2+ breast cancer who received chemotherapy and surgery were extracted from 2010 to 2020 from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was used to create well‐balanced cohorts for the NAT and AT groups. Kaplan–Meier (KM) analysis and Cox proportional hazards models were used to assess the differences between NAT and AT in terms of overall survival (OS) and breast cancer–specific survival (BCSS). Additionally, logistic regression models were used to explore factors associated with response to NAT.ResultsAfter PSM, 2140 patient pairs were successfully matched, which achieved a balanced distribution between the NAT and AT groups. KM curves revealed similar OS and BCSS between patients receiving NAT and those undergoing AT. A multivariate Cox model identified achieving pathological complete response (pCR) after NAT, compared with AT, as a protective prognostic factor for OS (hazard ratio, 0.52; 95% CI, 0.35–0.77; p &lt; .001) and BCSS (hazard ratio, 0.60; 95% CI, 0.37–0.98; p = .041). A logistic regression model revealed that White race and hormone receptor–negative status independently predicted pCR.ConclusionsFor patients with T1cN0M0‐stage HER2+ breast cancer, NAT demonstrated comparable OS and BCSS to AT. Patients who achieved pCR after NAT exhibited significantly better survival outcomes compared with those who received AT.

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

BackgroundBladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data. MethodsClinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database, combined with the data from The Cancer Genome Atlas and four databases from the Gene Expression Omnibus, which have available genomic data and clinical data, were selected. Gene expression data were transformed into gene sets data, and the performance of models based on clinical data and gene sets data and their combination were compared. Furthermore, the impact of model-derived scores on overall survival was evaluated. ResultsAmong 138,741 BCa patients with available clinical data, key independent predictors of survival included age, race, marital status, surgery, chemotherapy, radiation, and TNM stages. Clinical data machine learning (CML) models used these clinical predictors to achieve AUC values of 0.860, 0.821, and 0.804 in the testing sets for predicting survival at 1, 3, and 5 years, respectively. In the TAGO database, which has 863 patients with clinical and genomic data, the integrated clinical and gene expression machine learning model (IML) outperformed the CML and gene expression machine learning (GML) models in survival prediction. Patients with higher IML and GML model scores exhibited poorer survival outcomes. ConclusionsThis study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.

Abstract C155: Differential colorectal cancer mortality across racial and ethnic groups: Impact of socioeconomic status, clinicopathology, and treatment-related factors

Abstract Introduction: Non-Hispanic Black colorectal cancer (CRC) patients have a higher risk of mortality compared to most other racial/ethnic groups. Limited studies examine the contribution of socioeconomic (SES), clinicopathologic, or treatment variations toward mortality disparities. This retrospective cohort investigation examined the extent to which SES, clinicopathologic, and treatment factors explain racial/ethnic differences in CRC mortality. Methods: We studied 146,515 individuals ,18+ years old, with a confirmed diagnosis of CRC within 2010-2017 identified from the Surveillance, Epidemiology, and End Results (SEER) database. We performed Cox regression analyses to examine the association of race/ethnicity, surgery type, and tumor site with all-cause mortality and CRC-specific mortality. We then performed mediation analysis to quantify the extent to which mortality differences were mediated by SES, clinicopathologic, and treatment factors. Results: Non-Hispanic Black patients had a significantly higher hazard of all-cause mortality than non-Hispanic White patients. The non- Hispanic White versus non-Hispanic Black patients’ comparison demonstrated that variations in SES and clinicopathologic factors significantly explained 46.63% (indirect effect HR: 0.92, 95% CI 0.91-0.93) and 10.87% (indirect effect HR: 0.98, 95% CI 0.97-99) of the excess all-cause mortality among non-Hispanic Black patients, respectively. The Hispanic vs. non-Hispanic Black comparisons identified SES as the most influential mediator, explaining 19.68% of the excess all-cause mortality. The proportions mediating for CRC-specific mortality showed comparable outcomes to all-cause mortality. Conclusions Non-Hispanic Black patients had greater risk for all-cause mortality and CRC-specific mortality attributed to SES and clinicopathologic variations compared to other racial/ethnic groups. Future studies should investigate equity in healthcare through interventions addressing SES-related disparities. Citation Format: Pierre Fwelo, Toluwani E. Adekunle, Tiwaladeoluwa B. Adekunle, Ella R. Garza, Emily S. Huang, Wayne R. Lawrence, Aldenise P. Ewing. Differential colorectal cancer mortality across racial and ethnic groups: Impact of socioeconomic status, clinicopathology, and treatment-related factors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C155.

Construction and validation of a nomogram for predicting cancer-specific survival in middle-aged patients with advanced hepatocellular carcinoma: A SEER-based study.

Hepatocellular carcinoma is the predominant form of primary liver cancer and is the leading cause of cancer-related death. The aim of this study was to construct a nomogram to predict cancer-specific survival (CSS) in middle-aged patients with advanced hepatocellular carcinoma. Clinical data were downloaded from the Surveillance, Epidemiology and End Results (SEER) database for middle-aged patients diagnosed with advanced hepatocellular carcinoma (AJCC stage III and IV) from 2000 to 2019. The patients were randomized in a 7:3 ratio into training cohort and validation cohort. Univariate and multivariate Cox regression analyses were performed in the training cohort to screen for independent risk factors associated with cancer-specific survival for the construction of nomogram. The nomogram was examined and evaluated using the consistency index (C-index), area under the curve (AUC), and calibration plots. The clinical application value of the model was evaluated using decision curve analysis (DCA). A total of 3026 patients were selected, including 2244 in the training cohort and 962 in the validation cohort. Multivariate analysis revealed gender, marital status, American Joint Committee on Cancer (AJCC) stage, tumor size, bone metastasis, lung metastasis, alpha-fetoprotein (AFP) level, surgery, radiotherapy, chemotherapy as independent risk factors, which were all included in the construction of the nomogram. In the training cohort, the AUC values were 0.74 (95% CI: 0.76-0.72), 0.78 (95% CI: 0.82-0.75), and 0.82 (95% CI: 0.86-0.78) at 1-, 3-, and 5-year CSS, respectively. The calibration plots showed good consistency between the actual and predicted values. The DCA curves indicated that the nomogram model could more accurately predict CSS at 1-, 3-, and 5-year in middle-aged patients with advanced hepatocellular carcinoma compared with the AJCC staging system. Highly similar results to the training cohort were also observed in the validation cohort. In the risk stratification system, good differentiation was shown between the 2 groups, and Kaplan-Meier survival analysis indicated that surgery could prolong patient survival. In this study, we developed a nomogram and risk stratification system for predicting CSS in middle-aged patients with advanced hepatocellular carcinoma. The prediction model has good predictive performance and can help clinicians in judging prognosis and clinical decision making.

Interaction between tumor stage and age on survival outcomes of patients with anaplastic thyroid cancer.

Anaplastic thyroid cancer (ATC) is an aggressive, rare malignancy associated with rapid growth and metastasis, and a very poor prognosis. We investigated the clinical characteristics, survival outcomes and independent prognostic factors associated with anaplastic thyroid cancer. To assess to what extent the interaction between age and tumor stage affects mortality. A total of 622 patients diagnosed with anaplastic thyroid cancer, between 2010 and 2017 were enrolled in our study by retrieving data from the Surveillance, Epidemiology and End Results (SEER) database. We analyzed demographics, clinical characteristics, overall mortality (OM) and cancer specific mortality (CSM) of ATC. Variables with a P value < 0.1 were incorporated into the multivariate cox model to determine the independent prognostic factors. Furthermore, we analyzed the interaction between age and tumor stage on mortality. In the multivariate analyses, the divorced/separated population had a lower OM [hazard ratio (HR) = 0.63, 95%CI: 0.42-0.94, P < 0.05] and CSM (HR = 0.61, 95%CI: 0.40-0.92, P < 0.05). OM was higher in tumors with direct extension only (HR = 6.26, 95%CI: 1.29-30.42, P < 0.05) and tumors with distant spread (HR = 5.73, 95%CI: 1.34-24.51, P < 0.05). CSM was also higher in tumors with direct extension (HR = 5.05, 95%CI: 1.05-24.19, P < 0.05) and tumors with distant spread (HR = 4.57, 95%CI: 1.08-19.29, P < 0.05). Mortality was not adversely affected by lymph node involvement. OM was lower in patients who received radiation (HR = 0.66, 95%CI: 0.53-0.83, P < 0.01), chemotherapy (HR = 0.63, 95%CI: 0.50-0.79, P < 0.01) or surgery (HR = 0.53, 95%CI: 0.43-0.66, P < 0.01). CSM was also lower in patient who received radiation (HR = 0.64, 95%CI: 0.51-0.81, P < 0.01), chemotherapy (HR = 0.62, 95%CI: 0.50-0.78, P < 0.01) or surgery (HR = 0.51, 95%CI: 0.41-0.63, P < 0.01). There was no significant interaction between age and tumor stage that affected mortality. In this large US SEER database retrospective study, we found the mortality to be higher in advanced stage tumors with direct extension and distant metastasis. However, patients who received aggressive therapy showed a better overall survival. The aim of our study is to emphasize the importance of detecting ATC at an early stage and provide aggressive therapy to these patients. Since advanced stage ATC is associated with a dismal prognosis, we emphasize the need for randomized control trials and development of novel therapies that will be used to treat ATC.

Effect of radiation after surgery on the prognosis of children with Wilms tumor

Background To explore the association between radiation after surgery and the 5-year overall survival (OS) and 5-year cancer-specific survival (CSS) in patients with Wilms tumor. Methods In this cohort study, 1564 participants were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The univariate and multivariable COX proportional risk model as well as competitive risk model were used to explore the covariates associated with 5-year OS and 5-year CSS of patients with Wilms tumor and the correlation between radiation after surgery and 5-year OS or 5-year CSS of patients with Wilms tumor, respectively. The Kaplan-Meier curves of participants were plotted. Results The median follow-up was 126.00 (84.00, 178.00) months. Patients receiving surgery had higher 5-year survival probability than those not receiving surgery, while participants receiving radiation after surgery showed poor 5-year survival than those not. After adjusting for covariates including age and SEER stage, increased risk of 5-year overall mortality in patients with Wilms tumor [hazard ratio (HR) = 1.62, 95% confidence interval (CI): 1.10–2.41). After the adjustment for confounding factors including age, SEER stage and ethnicity, increased risk of 5-year cancer-specific mortality of patients with Wilms tumor was observed in those receiving radiation after surgery (HR = 1.77, 95%CI: 1.13–2.79). Conclusion Radiation after surgery was associated with poor prognosis of patients with Wilms tumor, which indicated that the clinicians should assess whether the patient was suitable for using radiation after surgery.

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.

Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions. We built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases. The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma. The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

Splenic flexure adenocarcinoma: A national cohort analysis of extent of surgical resection and outcomes.

The optimal extent of resection for splenic flexure adenocarcinoma remains debated. These tumours straddle the left- and right-sided vasculature with lymphatic drainage in a watershed area; current guidelines recommend either segmental or extended colectomy. We analysed surgical management of splenic flexure tumours and compared outcomes between approaches. The Surveillance, Epidemiology and End Results database was searched for adults with Stage I-III splenic flexure adenocarcinoma, 2004-2019. Of 5238 patients, 55% underwent extended colectomy. Compared to segmental colectomy, these patients were more likely to have advanced stage. On multivariable analysis, age ≤ 65 years remained independently associated with extended colectomy. Although fewer nodes were examined in segmental colectomy (median 14 vs. 16, p < 0.001), the number of positive nodes (both, median 0 [interquartile ratio 0-2], p = 0.20) and the lymph node ratio were similar between cohorts. Surgical approach was not significantly associated with increased positive nodal yield in adjusted analyses. Five-year overall and disease-specific survival were 73% and 84% for segmental and 72% and 83% for extended colectomy (p > 0.4); these remained comparable after adjustment. Nationally, we observed similar rates of segmental and extended colectomy for splenic flexure adenocarcinoma. Extended colectomy was not more common in Stage III disease, indicating lack of stage migration, and was not associated with better oncological outcomes. These observations support current practice involving either approach, which should be tailored to patient-related factors and preferences, while considering technical aspects and quality of life.

Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication

BackgroundTraditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients.MethodsData on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified.ResultsIn total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated.ConclusionThis study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Myelodysplastic syndrome/ Myeloproliferative disorder, Nos.

Background: Population-based research is limited for myelodysplastic syndrome/ myeloproliferative neoplasm, not otherwise specified (MDS/MPN-NOS) which comprises a heterogenous group of patients that do not fit into the other established MDS/MPN subgroups. Methods: We identified patients from the Surveillance, Epidemiology, and End Results (SEER) database with a diagnosis of MDS/MPN-U logged from 2010-2020. Age-standardized incidence rates (ASIR) and incidence rate ratios (IRR) were calculated using eight age subgroups. Subgroup analyses were performed for race and gender. Join point regression analyses were performed to model trends in ASIRs, and annual percent change (APC) was calculated. Results: A total of 2447 patients over age 50 were included in analysis. Incidence of MDS/MPN-U increased gradually between 2010-2016 (APC 12.90) then decreased between 2016-2020 (APC -19.56). ASIR was significantly higher in males compared to females in several age groups (age 65-69, 80-84, 85+). Conclusion: The underlying molecular complexity of these diseases remains a significant challenge. It is critical to identify MDS/MPN- specific therapeutic response criteria and suitable end points correlated with survival and AML risk for transformation to acute myeloid leukemia (AML) uniform assessment of treatment benefit.

Prognostic significance of ratio of positive lymph nodes in patients with operable major salivary ductal carcinoma

AbstractPurposeMajor salivary duct carcinomas (MSDCs) often involve the regional lymph nodes (LNs). However, the clinical value of LN parameters in patients with MSDCs is unclear. We aimed to investigate the optimal cut‐off points for number of positive LNs (PLNN) and ratio of positive LNs (PLNRs) and their prognostic value in patients with MSDC.Study DesignRetrospective cohort.MethodsWe retrospectively reviewed relevant data extracted from the Surveillance, Epidemiology, and End Results database on patients with MSDC who had undergone surgery between 2004 and 2016. The optimal PLNN and positive lymph node ratio (PLNR) cut‐off points were identified using the X‐tile program. Kaplan–Meier and Cox regression analyses were performed to determine prognostic factors.ResultsOverall, 290 patients were enrolled, 57.6% of whom had LN metastases. Advanced T stage in the submandibular gland and unpaired lesions were associated with LN involvement. Positive LNs, late T stage, and submandibular gland location were associated with poor overall survival (OS). The 5‐year OS rates of patients with negative and positive LNs were 74.3% and 36.5%, respectively. PLNN &gt; 16 and PLNR &gt; 0.48 were the best cut‐off points. The 5‐year OS of patients with PLNN ≤ 16 and PLNN &gt; 16 was 42.8% and 15.4%, respectively. The 5‐year OS rates were 46.8% for patients with PLNR ≤ 0.48 and 26.3% for patients with PLNR &gt; 0.48. PLNR was a strong prognostic factor for patients with MSDC with LN metastases.ConclusionsPLNR reflects both the effects of LN dissection and PLNN. Furthermore, its prognostic value in patients with MSDC exceeds that of PLNN. Patients with high PLNRs should be followed closely after surgery.

Survival benefits of radiotherapy in locally advanced unresectable and metastatic pancreatic cancer: a single-institution cohort and SEER database analysis.

Chemotherapy (CT) remains the primary treatment for locally advanced unresectable pancreatic cancer (LAUPC) and metastatic pancreatic cancer (MPC). The role of radiotherapy (RT) in these conditions remains unclear. This study compares the outcomes of CT alone versus CT combined with RT (combined-modality therapy [CMT]) in LAUPC and MPC patients. We conducted a retrospective analysis of LAUPC and MPC patients treated with either CT or CMT from a single institution and Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier curves and Cox hazards models evaluated the association between treatment modalities and overall survival (OS). Propensity score matching (PSM) ensured balanced comparisons. Landmark analysis addressed immortal time bias. Subgroup analyses were based on clinical characteristics. eXtreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP) assessed outcome prediction and influence of significant predictors. The study included 102 patients receiving CMT and 155 receiving CT at single institution, along with 1733 CMT and 9310 CT patients from the SEER dataset. In the single-institution cohort, CMT showed superior survival compared to CT both before (median OS: 20.5 vs. 11.5 months, hazard ratio [HR]: 0.47, 95% CI: 0.34-0.65, P=0.001) and after PSM (median OS: 22.2 vs. 11.8 months, HR: 0.49, 95% CI: 0.30-0.79, P=0.003). Multivariate analyses confirmed that CMT was independently associated with improved OS both before (HR: 0.54, 95% CI: 0.38-0.77, P=0.001) and after PSM (HR: 0.45, 95% CI: 0.27-0.73, P=0.001). Landmark analysis indicated better OS for patients receiving CMT compared to CT alone. Subgroup analysis revealed an OS benefit for CMT across most subgroups. SHAP value analysis indicated that CMT was the most significant contributor to survival outcomes. SEER database validation confirmed these findings. This study demonstrates that CMT significantly improves OS in LAUPC and MPC patients compared to CT alone. Integrating RT with CT could be beneficial for treating LAUPC and MPC.

Small bowel neuroendocrine tumors: Unique features and low lethality compared with small bowel adenocarcinoma.

Population-based studies comparing clinical characteristics and survival disparities between patients with small bowel adenocarcinoma (SBA) and small bowel neuroendocrine tumors (SBNETs) in the United States are limited. Data for patients with SBA or SBNETs, obtained from the Surveillance, Epidemiology, and End Results database for the years between 2000 and 2018 were analyzed. Between 2000 and 2018, the age-adjusted incidence of SBA experienced a marginal increase whereas SBNETs demonstrated a significant increase, emerging as the predominant subtype of small bowel cancer (SBC). Diagnoses peaked at ages 65-69years for SBA and 60-64years for SBNETs, with the latter exhibiting a heightened age-specific incidence and maintaining equilibrium in gender distribution. Clinicopathologic disparities revealed SBA's duodenal predilection, larger tumor size, and advanced stages, contrasting with SBNETs' ileal predilection, early-stage presentation, and superior outcomes. SBNETs patients underwent surgery more frequently but received less chemotherapy and radiation than SBA patients. Factors intricately correlated with a diagnosis of SBNETs included female gender, White race, advanced age, marital status, recent diagnoses, superior tumor differentiation, smaller size, distal location, and early-stage presentation. Survival analysis unveiled a remarkable 79% reduction in the mortality risk for SBNETs compared with SBA. Subgroup analysis further confirmed the consistently favorable survival advantages of SBNETs, highlighting the clinical relevance of histological classification in prognostication. Compared with SBA, SBNETs exhibited distinctive clinicopathological features characterized by a higher inclination toward low-grade and early-stage manifestations, thereby contributing to superior survival outcomes.

Development and validation of a prognostic nomogram for esophageal cancer patients based on SEER Asian population

This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification.

Early death incidence and prediction in stage IV large cell neuroendocrine carcinoma of the lung.

Nearly half of lung large cell neuroendocrine carcinoma (LCNEC) patients are diagnosed at an advanced stage and face a high early death risk. Our objective was to develop models for assessing early death risk in stage IV LCNEC patients. We used surveillance, epidemiology, and end results (SEER) databases to gather data on patients with stage IV LCNEC to construct models and conduct internal validation. Additionally, we collected a dataset from the Second Affiliated Hospital of Nanchang University for external validation. We used the Pearson correlation coefficient and variance inflation factor to identify collinearity among variables. Logistic regression analysis and least absolute shrinkage and selection operator analysis were employed to identify important independent prognostic factors. Prediction nomograms and network-based probability calculators were developed. The accuracy of the nomograms was evaluated using receiver operating characteristic curves. The goodness of fit of the nomograms was evaluated using the Hosmer-Lemeshow test and calibration curves. The clinical value of the models was assessed through decision curve analysis. We enrolled 816 patients from the surveillance, epidemiology, and end results database and randomly assigned them to a training group and a validation group at a 7:3 ratio. In the training group, we identified 9 factors closely associated with early death and included them in the prediction nomograms. The overall early death model achieved an area under the curve of 0.850 for the training group and 0.780 for the validation group. Regarding the cancer-specific early death model, the area under the curve was 0.853 for the training group and 0.769 for the validation group. The calibration curve and Hosmer-Lemeshow test both demonstrated a high level of consistency for the constructed nomograms. Additionally, decision curve analysis further confirmed the substantial clinical utility of the nomograms. We developed a reliable nomogram to predict the early mortality risk in stage IV LCNEC patients that can be a helpful tool for health care professionals to identify high-risk patients and create personalized treatment plans.

Incidence, survival, and prognostic nomogram of patients with small intestinal neuroendocrine tumors: A SEER population-based study.

Small intestinal neuroendocrine tumors (SI-NETs) are a group of rare and significantly heterogeneous tumors with limited research currently available. This study aimed to investigate the incidence, survival, and prognostic factors of SI-NETs. We selected data from the surveillance, epidemiology, and end results (SEER) database between 2000 and 2019 and evaluated the incidence trend of SI-NETs during this period. We utilized the Kaplan-Meier method to examine the association between clinical variables and survival rates. Based on the multivariable Cox regression analysis results, we developed a nomogram to predict the 1-, 2-, and 3-year cancer-specific survival (CSS) of SI-NETs patients. We evaluated the consistency, accuracy, and clinical utility of the nomogram by drawing calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) curves. The incidence of SI-NETs showed an upward trend in recent years. Age, grade, T stage, M stage, and primary tumor surgery were independent risk factors for CSS in SI-NETs patients. The nomogram model based on these risk factors showed high accuracy and clinical benefit. SI-NETs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in SI-NETs patients, which may benefit clinical decision-making.

Radiotherapy is recommended for hormone receptor-negative older breast cancer patients after breast conserving surgery

In this study, the necessity of radiotherapy (RT) for hormone receptor-negative older breast cancer patients after breast-conserving surgery (BCS) was investigated. The data of hormone receptor-negative invasive breast cancer patients who underwent BCS were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. All patients were separated into two groups, namely, the RT group and the no radiotherapy (No RT) group. The 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates were compared between the No RT and RT groups after propensity score matching (PSM). The nomograms for predicting the survival of patients were constructed from variables identified by univariate or multivariate Cox regression analysis. A total of 2504 patients were enrolled in the training cohort, and 630 patients were included in the validation cohort. After PSM, 738 patients were enrolled in the No RT group and RT group. We noted that RT can improve survival in hormone receptor-negative older breast cancer patients who undergo BCS. Based on the results of multivariate Cox analysis, age, race, tumour grade, receipt of RT and chemotherapy, pathological T stage, N status, M status and HER2 status were linked to OS and CSS for these patients, and nomograms for predicting OS and CSS were constructed and validated. Moreover, RT improved OS and CSS in hormone receptor-negative older breast cancer patients who underwent BCS. In addition, the proposed nomograms more accurately predicted OS and CSS for hormone receptor-negative older breast cancer patients after BCS.

Enhanced Disease-Specific Survival Among Individuals With Malignant Adnexal Tumors of the Skin Treated With Mohs Surgery: A National Database Study.

Malignant adnexal tumors of the skin are a rare group of tumors that can be locally aggressive and require surgical excision with strict margin control to achieve clearance. Given the rarity of these tumors, there is a limited understanding within the medical community regarding optimal treatment approaches. To examine surgical management trends and outcomes for patients diagnosed with cutaneous adnexal tumors from 2000 to 2020. The Surveillance, Epidemiology, and End Results database was queried to identify biopsy-proven cases of cutaneous adnexal tumors between 2000 and 2020. Clinical and disease characteristics were examined, and disease-specific survivals were compared between surgical approaches using Kaplan-Meier curves and Cox proportional hazards models. Mohs surgery demonstrated a significant increase in utilization from 2000 to 2020 (+447.1%) and improvements in disease-specific survival (mean 231.7 months; p < .001) compared with no surgery; wide local excision exhibited no improved disease-specific survival (227.7 months; p = .070). Multivariable Cox regression further highlighted that only Mohs surgery exhibited a reduced disease-specific mortality risk compared with no surgery (adjusted HR 0.49; p = .011). Given the enhanced disease-specific survival coupled with tissue preservation strategies, Mohs surgery emerges as a promising surgical approach for the treatment of malignant adnexal tumors of the skin.