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Abstract Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed.

Abstract 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.

Abstract Background Traditional 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. Methods Data 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. Results In 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. Conclusion This 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.

Abstract The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

Abstract 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

Abstract To examine the potential correlation between chemotherapy and the risk of individual of second primary endometrial cancer (SEC) in patients with rectal cancer (RC) and assess survival outcomes. The study employed the Surveillance, Epidemiology, and End Results database (SEER) as the primary data source, it encompasses a substantial cohort of patients diagnosed with RC between 1975 and 2018. This study involved a total of 30,847 individuals diagnosed with RC, of whom 168 individuals (5.45‰) experienced SEC. Among them, 107 patients (3.47‰) received chemotherapy treatment, while 61 patients (1.98‰) did not receive chemotherapy. The analysis of the overall occurrence of SEC revealed a significant association between SEC and chemotherapy treatment. Univariate and multivariate analyses confirmed a significant association between chemotherapy treatment and an increased risk of developing SEC in RC patients. Upon implementation of a dynamic analysis on the variables of relative risk and standardized incidence ratios, the results revealed that the likelihood of SEC escalated in tandem with advancing age. The examination of patients who developed SEC after receiving and not receiving chemotherapy revealed no substantial disparities in the 10-year overall survival (OS) and (cancer-specific survival) CSS rates. The results were the same after propensity score matching. Nevertheless, a notable discrepancy emerged when comparing the OS and CSS rates at 10 years between patients afflicted with SEC subsequent to chemotherapy and those afflicted with primary endometrial cancer, and the result was the same situation in the no-chemotherapy group. The use of chemotherapy in RC patients has been associated with an increased probability of developing specific SEC. Therefore, it is imperative to prioritize efforts aimed at reducing chemotherapy-related SEC occurrences and improving the prognosis of affected individuals.

Abstract Background Comprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking. Patient and method The surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration. Result 577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM. Conclusion The competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe.

Introduction: Uterine carcinosarcoma is an aggressive gynecologic malignancy that accounts for 5% of all gynecological malignancies. There is a disproportion in its incidence and mortality among different races. This study describes demographic and clinicopathological factors and racial disparities affecting the survival of patients with uterine carcinosarcoma. Methods: Data on uterine carcinosarcoma patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2020. Results: Of the 11,338 patients identified, the median age at diagnosis was 68 years, and the five-year cause-specific survival (CSS) rate was 38.7%. for all races. Compared with Asian patients (39.5%, 95% CI, 36.0–43.4%), Hispanic patients (39.4%, 95% CI, 36.5–42.5%), and White patients (37.9%, 95% CI, 36.7–39.2%), Black patients accounted for 21% of the patients and had a significantly lower 5-year CSS (95% CI, 27.2–31.2%). The CSS rates were 84.4% (95% CI, 83.3–85.6%) for localized tumors, 68.5% (95% CI, 66.9–70.1%) for regional tumors, and 39.0% (95% CI, 36.9–41.2%) for distant tumors. Multimodal treatment involving chemotherapy, surgery, and radiation improved the overall one- and five-year survival rates by 88.2% (95% CI, 87.0–89.5%) and 52.8% (95% CI, 50.7–55.1%), respectively, across all disease stages. Multivariate analysis identified age >60 years, Black race, tumor size >4 cm, and distant metastases as independent risk factors for mortality (p < 0.0001). Conclusions: This large database study presents the most up-to-date epidemiological information regarding cases of uterine carcinosarcoma. The findings suggest that a combination of surgery, chemotherapy, and radiation may be most efficacious in treating this malignancy, especially in patients with distant disease.

Abstract Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan–Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients’ overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P

Abstract Introduction Laryngeal cancers account for one-third of all head and neck cancers. We aimed to report the incidence trends of laryngeal cancer over 2000–2020 in the United States (US), by age, sex, race/ethnicity, and histological subtypes. Methods Data from the Surveillance, Epidemiology, and End Results 22 database were used to identify patients with laryngeal cancer based on the International Classification of Diseases for Oncology, version 3. Age-standardized incidence rates (ASIRs) for laryngeal cancer, adjusted for reporting delays, were calculated. The Joinpoint Regression Program was then utilized to determine annual percent changes (APCs) and average annual percent changes (AAPCs) in the trends. The analysis excluded data from 2020 to prevent potential bias related to the COVID-19 pandemic. Results A total of 104,991 cases of laryngeal cancer were identified in the US from 2000 to 2019. Squamous cell carcinoma was the predominant subtype, accounting for 94.53% of cases. Above 73.20% occurred among non-Hispanic whites, with the highest incidence observed among individuals aged 55–69 years (46.71%). The ASIRs were 5.98 and 1.25 per 100,000 population for men and women, respectively. Over 2000–2019, there was a significant reduction in ASIRs for laryngeal cancer in both sexes. Non-Hispanic black men exhibited the highest ASIR (9.13 per 100,000) and the largest decline in the ASIRs over 2000–2019 (AAPC: -3.26%). Conclusions Laryngeal cancer incidence rates showed a decline from 2000 to 2019, in addition to 2020, during the COVID-19 pandemic. Additional research is required to investigate risk factors and their influence on incidence rates of laryngeal cancer.

Abstract Purpose To address this evidence gap and validate short-term OS at less than 5 years as a reliable surrogate endpoint for 5-year OS. Methods We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database, focusing on non-metastatic NPC patients diagnosed between 2010 and 2015. Patients were categorized into radiotherapy and chemoradiotherapy groups. Results This retrospective study examined 2,047 non-metastatic NPC patients. Among them, 217 received radiotherapy, and 1,830 received chemoradiotherapy. Our analysis results indicated that the 4-year OS may serve as a reliable surrogate endpoint for patients with AJCC clinical stage I (80 vs. 78%, P = 0.250), regardless of the treatment received. Specifically, in the radiotherapy group, patients with stage I, T0-T1, and N0 NPC showed similar OS rates at 4 and 5 years (83 vs. 82%, P = 1.000; 78 vs. 76%, P = 0.250; 78 vs. 77%, P = 0.500, respectively). Similarly, patients with stage II-IV, T2-T4, and N1-3 NPC showed no significant difference in OS rates between 3 and 5 years (57 vs. 51%, P = 0.063; 52 vs. 46%, P = 0.250; 54 vs. 46%, P = 0.125, respectively) in the radiotherapy group. In the chemoradiotherapy group, only the 3-year OS rate did not significantly differ from that at 5 years in stage I patients (79vs. 72%, P = 0.063). Conclusions Our study suggests that short-term surrogate endpoints may be valuable for evaluating 5-year OS outcomes in NPC patients in non-endemic areas.

Introduction: Right hemicolectomy (RHC) remains the treatment standard for goblet cell adenocarcinoma (GCA), despite limited evidence supporting survival benefit. This study aims to explore factors influencing surgical management and survival outcomes among patients treated with RHC or appendicectomy using NCRAS (UK) and SEER (USA) data. Methods: A retrospective analysis was conducted using 998 (NCRAS) and 1703 (SEER) cases. Factors influencing procedure type were explored using logistic regression analyses. Overall survival (OS) probabilities and Kaplan–Meier (KM) plots were generated using KM analysis and the log-rank test compared survival between groups. Cox regression analyses were performed to assess hazard ratios. Results: The NCRAS analysis revealed that age and regional stage disease were determinants of undergoing RHC, with all age groups showing similar odds of receiving RHC, excluding the 75+ age group. The SEER analysis revealed tumour size > 2 cm, and receipt of chemotherapy were determinants of undergoing RHC, unlike the distant stage, which was associated with appendicectomy. Surgery type was not a significant predictor of OS in both analyses. In NCRAS, age and stage were significant predictors of OS. In SEER, age, stage, and Black race were significant predictors of worse OS. Conclusions: The study shows variations in the surgical management of GCA, with limited evidence to support a widespread recommendation for RHC.

Abstract Few studies have investigated the impact of primary tumor resection (PTR) on patients with distant metastasis medullary thyroid carcinoma (DMMTC). This population-based study aims to assess the application of PTR in DMMTC patients, ascertain its benefits, and identify optimal surgical indications. DMMTC Patients diagnosed between 2010 and 2020 were included through the Surveillance, Epidemiology, and End Results (SEER) program. Logistic regression analysis identified driving factors of surgical decision-making. Propensity score matching (PSM), Kaplan–Meier method, and Cox regression were utilized to compare overall survival (OS) and disease-specific survival (DSS) between surgical and non-surgical groups. Subgroup analyses were performed to determine optimal surgical indications. Of 238 DMMTC patients included, 122 (51.3%) patients underwent PTR. Extrathyroidal extension and N1 stage emerged as independent factors promoting the surgical decision. PSM-adjusted survival analyses revealed significant advantages in both OS and DSS for the surgical group. Moreover, subgroup analyses indicated that except for patients aged ≥ 65 years, tumors ≤ 20 mm, or with multiple metastasized sites (> 1), the others significantly benefit from PTR. PTR significantly improves prognosis in selected DMMTC patients. The decision to undergo PTR in other patients should be based on a comprehensive assessment of the disease, surgeon’s experience, and family discussions for potential survival benefits.

Abstract Objective The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified. Methods From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve. Results Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P

Abstract Currently, there is no consensus regarding the extent of surgery for stage I pulmonary carcinoid (PC) tumors, which encompass typical carcinoid (TC) and atypical carcinoid (AC) tumors. Sublobar resection includes segmental resection and wedge resection; the former is regarded as a type of anatomical resection that is better suited for tumor treatment. Therefore, it needs to be further verified whether differences exist in the effects of the two surgical methods on the survival time of patients. Propensity score matching (PSM) was used. The primary endpoints were cancer-specific survival (CSS) and overall survival (OS) time. Survival differences were analyzed via the Kaplan–Meier method and the log-rank test. There was no significant difference in survival between the sublobar resection and lobectomy groups after PSM in either the TC or AC tumor groups (all p > 0.05). A total of 1680 patients underwent pulmonary wedge resection (TC: n = 1547, AC: n = 133), and 398 patients underwent segmental resection (TC: n = 365, AC: n = 33). After PSM, there were no statistically significant differences in survival, regardless of whether OS or CSS was considered the primary endpoint (OS: p = 0.337; CSS: p = 0.470). Furthermore, segmental resection did not prolong patient survival time compared with wedge resection in different subgroup analyses on the basis of histology, age, and tumor size (all p > 0.05). Finally, the same results were obtained via multivariate Cox analysis (OS: p = 0.153; HR = 1.21; CSS: p = 0.351, HR = 1.32). Sublobar resection could be considered for patients with early-stage typical or atypical pulmonary carcinoid, provided that a rigorous lymph node evaluation is conducted. If the tumor is distant from the pulmonary hilum, either segmentectomy or wedge resection may be performed depending on the specific location of the tumor and the clinical condition of the patient.

Abstract We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions.

Abstract Background and Purpose The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical. Results 3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan–Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant. Conclusions The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans.

Background: Spinal Ependymoma (SP-EP) is the most commonly occurring tumor affecting the spinal cord. Prompt diagnosis and treatment can significantly enhance prognostic outcomes for patients. In this study, we conducted a comprehensive analysis of RNA sequencing data, along with associated clinical information, from patients diagnosed with SP-EP. The aim was to identify key genes that are characteristic of the disease and develop a survival-related nomogram. Methods: We first accessed the Gene Expression Integrated Database (GEO) to acquire the microarray dataset pertaining to SP-EP. This dataset was then processed to identify differentially expressed genes (DEGs) between SP-EP samples and normal controls. Furthermore, machine learning techniques and the CIBERSORT algorithm were employed to extract immune characteristic genes specific to SP-EP patients, thereby enhancing the characterization of target genes. Next, we retrieved comprehensive information on patients diagnosed with SP-EP between 2000 and 2020 from the Surveillance, Epidemiology, and End Results Database (SEER). Using this data, we screened for predictive factors that have a significant impact on patient outcomes. A nomogram was constructed to visualize the predicted overall survival (OS) rates of these patients at 3, 5, and 8 years post-diagnosis. Finally, to assess the reliability and clinical utility of our predictive model, we evaluated it using various metrics including the consistency index (C-index), time-dependent receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: A total of 5,151 DEGs were identified between the SP-EP sample and the normal sample. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that these DEGs were primarily involved in cellular processes, including cell cycle regulation and cell sensitivity mechanisms. Furthermore, immune infiltration analysis was utilized to identify the core gene CELF4. Regarding the survival rates of patients with SP-EP, the 3-year, 5-year, and 8-year survival rates were 72.5, 57.0, and 40.8%, respectively. Diagnostic age (p < 0.001), gender (p < 0.001), and surgical approach (p < 0.005) were identified as independent prognostic factors for OS. Additionally, a nomogram model was constructed based on these prognostic factors, demonstrating good consistency between predicted and actual results in the study's validation process. Notably, the study also demonstrated that more extensive surgical resection could extend patients' OS. Conclusion: Through bioinformatics analysis of microarray datasets, we identified CELF4 as a central gene associated with immune infiltration among DEGs. Previous studies have demonstrated that CELF4 may play a pivotal role in the pathogenesis of SP-EP. Furthermore, this study developed and validated a prognostic prediction model in the form of a nomogram utilizing the SEER database, enabling clinicians to accurately assess treatment risks and benefits, thereby enhancing personalized therapeutic strategies and prognosis predictions. [ABSTRACT FROM AUTHOR]

Background: The real-time prognostic data of patients with poorly differentiated thyroid carcinoma (PDTC) after surviving for several years was unclear. This study aimed to employ a novel method to dynamically estimate survival for PDTC patients. Methods: A total of 913 patients diagnosed with PDTC between 2014 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, was recruited in our study. Kaplan--Meier method was used to estimate the overall survival (OS). The conditional survival (CS) outcomes of PDTC were analyzed and CS rates were calculated using the formula CS(y/x) = OS(y+x)/OS(x), whereby CS(y/x) denotes the probability of a patient enduring an additional y years subsequent to surviving x years following the diagnosis of PDTC. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify prognostic predicters and multivariate Cox regression was utilized to develop a CS-nomogram. Finally, the performance of this model was evaluated and validated. Results: Kaplan--Meier survival analysis unveiled patient outcomes demonstrating an OS rate of 83%, 75%, and 60% respectively at the end of 3, 5, and 10 years. The novel CS analysis highlighted a progressive enhancement in survival over time, with the 10-year cumulative survival rate progressively augmenting from its initiation of 60% to 66%, 69%, 73%, 77%, 81%, 83%, 88%, 93%, and finally 97% (after surviving for 1-9 years, respectively) each year. And then 11 (11/15) predictors including age at diagnosis, sex, histology type, SEER stage, T stage, N stage, M stage, tumor size, coexistence with other malignancy, radiotherapy and marital status, were selected by LASSO analysis under the condition of lambda.min. Multivariate Cox regression analysis further highlighted the significant impact of all these predictors on the OS of PDTC and we successfully established and validated a novel CS-nomogram for real-time and dynamic survival prediction. Conclusions: This was the first study to analyze the CS pattern and demonstrate a gradual improvement in CS over time in long-term PDTC survivors. We then successfully developed and validated a novel CS-nomogram for individualized, dynamic, and real-time survival forecasting, empowering clinicians to adapt and refine the patient-tailored treatment strategy promptly with consideration of evolving risks. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify prognostic factors for survival and develop a prognostic nomogram to predict the survival probability of elderly patients with lower-grade gliomas (LGGs). Methods: Elderly patients with histologically confirmed LGG were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. These individuals were randomly allocated to the training and validation cohorts at a 2:1 ratio. First, Kaplan-Meier survival analysis and subgroup analysis were performed. Second, variable screening of all 13 variables and a comparison of predictive models based on full Cox regression and LASSO-Cox regression analyses were performed, and the key variables in the optimal model were selected to construct prognostic nomograms for OS and CSS. Finally, a risk stratification system and a web-based dynamic nomogram were constructed. Results: A total of 2307 elderly patients included 1220 males and 1087 females, with a median age of 72 years and a mean age of 73.30 ± 6.22 years. Among them, 520 patients (22.5%) had Grade 2 gliomas, and 1787 (77.5%) had Grade 3 gliomas. Multivariate Cox regression analysis revealed four independent prognostic factors (age, WHO grade, surgery, and chemotherapy) that were used to construct the full Cox model. In addition, LASSO-Cox regression analysis revealed five prognostic factors (age, WHO grade, surgery, radiotherapy, and chemotherapy), and a LASSO model was constructed. A comparison of the two models revealed that the LASSO model with five variables had better predictive performance than the full Cox model with four variables. Ultimately, five key variables based on LASSO-Cox regression were utilized to develop prognostic nomograms for predicting the 1-, 2-, and 5-year OS and CSS rates. The nomograms exhibited relatively good predictive ability and clinical utility. Moreover, the risk stratification system based on the nomograms effectively divided patients into low-risk and high-risk subgroups. Conclusion: Variable screening based on LASSO-Cox regression was used to determine the optimal prediction model in this study. Prognostic nomograms could serve as practical tools for predicting survival probabilities, categorizing these patients into different mortality risk subgroups, and developing personalized decision-making strategies for elderly patients with LGGs. Moreover, the webbased dynamic nomogram could facilitate its use in the clinic. [ABSTRACT FROM AUTHOR]

Background: Primary malignant melanoma (MM) of skin threatens health, especially in the older population, causing a significant risk of early death. The purpose of this study was to establish a diagnostic nomogram to predict the early mortality risk in older patients with primary skin MM and to determine the independent risk factors of cancer-specific early death in such patients. Methods: The Surveillance, Epidemiology and End Results (SEER) database provided the clinical and pathological characteristics of older patients with primary skin MM from 2000 to 2019. Initially, a 7:3 random assignment was used to place the recruited patients into training and validation cohorts. Then, the independent risk variables of cancer-specific early death in those individuals were determined using univariate and multivariate logistic regression analysis. Those patients' diagnostic nomograms were constructed using the acquired independent risk variables. Ultimately, the performance of the newly created diagnostic nomogram was verified using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Results: In this study, 2,615 patients in total were included. Age, histology, liver metastasis, tumor stage, surgery, therapy, and radiation were found to be independent risk factors following statistical analysis, with a special emphasis on early death in older patients with primary skin MM. A diagnostic nomogram for the cancer-specific early death risk was created and validated based on these variables. High agreement was reported between the expected and actual probabilities in the calibration curves. Area under the curves (AUC) of the novel created diagnostic nomogram was greater than that of each independent risk factor, with AUCs for the training and validation cohorts being 0.966 and 0.971, respectively. The nomogram had a high value for its applicability in clinical settings, according to DCA. Conclusion: In older patients with primary skin MM, the current study created a diagnostic nomogram to predict the probability of cancer-specific early death. Because of the nomograms' good performance, physicians will be better able to identify older patients who are at a high risk of early death and treat them individually to increase their survival benefit. [ABSTRACT FROM AUTHOR]

Background: Traditional 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. Methods: Data 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. Results: In 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. Conclusion: This 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. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence and mortality rates of cancer in the Surveillance, Epidemiology, and End Results (SEER) Program for adolescent and young adult (AYA) patients show distinct patterns among early-onset cancers. For some cancers, AYA cancer rates varied by age group, sex, race, ethnicity and geography. Monitoring the rates and time trends of AYA cancer emphasizes the distinct health concern for this age group. (1) Background: The incidence rate of early onset-cancer (<50) has increased since 1995. Among younger people, cancers in AYAs (aged 15–39 y) are often biologically distinct tumors from those treated in the pediatric and older adult population. The current study describes trends in the United States for the most recent years including the first year of the COVID-19 epidemic. We aimed to describe the recent incidence and mortality trends of cancers in AYAs (aged 15–39 y). (2) Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER 22) from 1 January 2016 to 31 December 2021. Age-adjusted incidence and mortality rates were assessed by SEER*Stat 8.4.3 for major cancer types by sex, race/ethnicity, age, and metropolitan/nonmetropolitan status. Time trends of age-adjusted incidence and mortality rates were examined by sex and metropolitan/nonmetropolitan status. (3) Results: Age-adjusted overall cancer incidence and mortality rates were stable during this study period. The age-adjusted incidence rates declined significantly for ependymoma, melanoma, carcinomas of lung, bronchus, and trachea, unspecified malignant neoplasms, and non-Hodgkin's lymphoma. Significant increases were found for gastrointestinal tract cancers and non-Kaposi sarcomas. The age-adjusted mortality rate decreased for acute myeloid leukemia, melanoma, carcinomas of liver and intrahepatic bile ducts, kidney and, in women, leukemia. For some cancers, rates differed by sex, race, ethnicity, and geography. Monitoring the rates and time trends of AYA cancer emphasizes the distinct health concern for this age group. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The necessity and therapeutic value of lymph node dissection (LND) in early stage T1 MTC patients remain controversial. Methods: Patients with T1MTC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Poisson regression analysis was utilized to investigate promotive factors for lymph node metastasis in T1MTC patients. Fisher's exact test was employed to calculate baseline differences between non-LND and LND groups. Propensity score match (PSM) was used to control baseline bias. Survival outcomes were calculated by KaplanMeier method and log-rank test. Multivariable Cox regression assessed the prognostic impact of LND across subgroups. Results: Of 3298 MTC cases, 50.4% were T1MTC. The lymph node metastasis rate increased along with the T stage (from 22.2% to 90.5%). Among 1231 T1MTC patients included after exclusion criteria, 72.0% underwent LND and 22.0% had lymph node metastasis. Patients aged younger than 44 years (RR=1.700, p<0.001), male (RR=1.832, p<0.001), and with tumor larger than 10mm (RR=2.361, p<0.001) were more likely to have lymph node metastasis, while elderly patients (p<0.001) and those with microcarcinoma (p<0.001) were more likely to undergo non-LND procedures. LND provided no OS or DSS benefit over non-LND before and after propensity score match (matched 10-year OS/DSS: LND 83.8/96.2% vs non-LND 81.9/99.3%, p>0.05). Subgroup analyses revealed no prognostic gain with LND in any subgroup (p>0.05). Conclusion: Nearly half of MTC patients were diagnosed at T1 stage and had low lymph node risk. Different from ATA guidelines, avoiding routine LND conferred similar prognosis to standard procedures while potentially improving quality of life. Large-scale prospective multi-center studies should be conducted to further validate these findings. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to develop a nomogram model for individual prognosis prediction in patients with hormone receptors positive (HR+) mucinous breast carcinoma (MBC) and assess the value of neoadjuvant chemotherapy (NAC) in this context. Methods: A total of 6,850 HR+ MBC patients from the SEER database were identified and randomly (in a 7:3 ratio) divided into training cohorts and internal validation cohorts. 77 patients were enrolled from the Chongqing University Cancer Hospital as the external validation cohort. Independent risk factors affecting overall survival (OS) were selected using univariate and multivariate Cox regression analysis, and nomogram models were constructed and validated. A propensity score matching (PSM) approach was used in the exploration of the value of NAC versus adjuvant chemocherapy (AC) for long-term prognosis in HR+ MBC patients. Results: Multivariate Cox regression analysis showed 8 independent prognostic factors: age, race, marital status, tumor size, distant metastasis, surgery, radiotherapy, and chemotherapy. The constructed nomogram model based on these 8 factors exhibited good consistency and accuracy. In the training group, internal validation group and external validation group, the high-risk groups demonstrated worse OS (p<0.0001). Subgroup analysis revealed that NAC had no impact on OS (p = 0.18), or cancer specific survival (CSS) (p = 0.26) compared with AC after PSM. Conclusions: The established nomogram model provides an accurate prognostic prediction for HR+ MBC patients. NAC does not confer long-term survival benefits compared to AC. These findings provide a novel approach for prognostic prediction and clinical practice. [ABSTRACT FROM AUTHOR]

(1) Background: This study seeks to employ a machine learning (ML) algorithm to forecast the risk of distant metastasis (DM) in patients with T1 and T2 gallbladder cancer (GBC); (2) Methods: Data of patients diagnosed with T1 and T2 GBC was obtained from SEER, encompassing the period from 2004 to 2015, were utilized to apply seven ML algorithms. These algorithms were appraised by the area under the receiver operating characteristic curve (AUC) and other metrics; (3) Results: This study involved 4371 patients in total. Out of these patients, 764 (17.4%) cases progressed to develop DM. Utilizing a logistic regression (LR) model to identify independent risk factors for DM of gallbladder cancer (GBC). A nomogram has been developed to forecast DM in early T-stage gallbladder cancer patients. Through the evaluation of different models using relevant indicators, it was discovered that Random Forest (RF) exhibited the most outstanding predictive performance; (4) Conclusions: RF has demonstrated high accuracy in predicting DM in gallbladder cancer patients, assisting clinical physicians in enhancing the accuracy of diagnosis. This can be particularly valuable for improving patient outcomes and optimizing treatment strategies. We employ the RF algorithm to construct the corresponding web calculator. [ABSTRACT FROM AUTHOR]

Background and Aim: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. Methods: In this population‐based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long‐short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. Results: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897–0.916), 0.908 (95% CI 0.899–0.918), 0.910 (95% CI 0.901–0.919), and 0.793 (95% CI 0.769–0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Conclusions: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the impacts of the number of positive lymph nodes (NPLN) and lymph node ratio (LN ratio) for patients with hypopharyngeal squamous cell carcinoma (HPSCC) based on SEER database, which were validated in the real-world data of China. Methods: A total of 520 patients from SEER database were analyzed. Then 195 patients with pathologically stage III or IV HPSCC in our center were retrospectively studied. Results: In the SEER database, NPLN ≥ 3 was found in 36.9% of patients. Multivariate analysis revealed that LN ratio ≥ 0.138 was significant with poorer overall survival (OS) (hazard ratio [HR] = 1.525, p = 0.001) and cancer-specific survival (CSS) (HR = 1.697, p < 0.001), so was the NPLN ≥ 3 (HR = 1.388, p = 0.013; HR = 1.479, p = 0.008). Patients with NPLN ≥ 3 were found in 103 (52.8%) in our center. Multivariate analysis confirmed a significant association regarding OS (p = 0.005) or CSS (p = 0.003) between patients with LN ratio ≥ 0.138 or not. In addition, disease recurrence rate differed significantly between the patients with NPLN ≥ 3 (27.2%) and NPLN < 3 (14.1%, p = 0.026). Moreover, postoperative chemoradiotherapy (CCRT) was significantly associated with better prognosis in patients with NPLN ≥ 3. Conclusion: In the SEER database, NPLN ≥ 3 and LN ratio ≥ 0.138 were independent poor prognostic factors for patients with HPSCC. Whereas identifying worldwide cut-off values for LN ratio is difficult and surgeon-dependent. In our cohort, adjuvant CCRT was beneficial for OS in patients with NPLN ≥ 3. [ABSTRACT FROM AUTHOR]

Limited knowledge exists regarding lung metastases from cancer of unknown primary (CUPL), particularly concerning young patients. This study aims to investigate the clinicopathologic features and prognostic factors of CUPL patients, with a specific focus on comparing the survival outcomes across different age groups. We conducted a retrospective analysis of patients diagnosed with CUPL between 2010 and 2020, utilizing the SEER database. Clinical characteristics among different age groups were compared. Prognostic factors influencing overall survival (OS) in CUPL patients were assessed through Cox regression analysis, while competing risks analysis was employed to evaluate cancer-specific survival (CSS) prognostic factors. A comparison of survival differences between age groups was conducted utilizing the Kaplan–Meier and Cumulative Incidences Function. A total of 2,474 patients with CUPL were included in this study, predominantly in the middle-aged and elderly demographic. The median survival time was a mere 1 month, with a one-year OS rate of 11 % and a one-year CSS rate of 13.8 %. Age, tumor histological typing and grading, liver metastasis, bone metastasis, radiotherapy, and chemotherapy were identified as independent prognostic factors affecting both OS and CSS. Despite the small representation of young patients (<40 years old) at 3 %, their OS and CSS rates significantly surpassed those of middle-aged (40–70 years old) and elderly patients (>70 years old). This advantage persists among patients undergoing radiation and chemotherapy. While exceedingly uncommon among young patients, the prognosis for survival is more favorable than in middle-aged and elderly patients. Administration of radiotherapy and chemotherapy emerges as a potential avenue to enhance the survival prognosis for CUPL patients. [ABSTRACT FROM AUTHOR]

Background: Large population-based registries, such as the Surveillance, Epidemiology and End Results (SEER) Registry, help in the study of rare tumors, including medullary thyroid cancer (MTC), but lack data to understand the natural history of the disease. The Medullary Thyroid Cancer Collaborative Registry (MTCCoRe) is an exhaustive multi-institutional collection of demographic, clinical, and pathological data. To determine the extent to which MTCCoRe represents the real-world MTC population, we compared the characteristics of patients enrolled in MTCCoRe with patients enrolled in population-based cancer registries. Methods: Comparison of demographic and clinical characteristics of MTC patients who were enrolled in MTCCoRe, Texas Cancer Registry (TCR), California Cancer Registry (CCR), and SEER between 1995 and 2018. Results: A total of 1416 patients were identified in MTCCoRe, 329 in TCR, 2105 in CCR, and 3820 in SEER. Percentages of patients 20–54 years in MTCCoRe were 58.0%, 50.2% in TCR, 47.2% in CCR, and 44.8% in SEER (p < 0.0001). About half of the patients were female (55.9% in MTCCoRe, 61.4% in TCR, 59% in CCR, and 57.5% in SEER (p = 0.3). Percentages of Hispanic and Black patients differed among cohorts (10.1% and 3.8% for MTCCoRe, 23.7% and 8.2% for TCR, 24.8% and 4.9% in CCR, and 15.9% and 8.2% for SEER, respectively; p < 0.001). MTCCoRe patients presented with more advanced T and N classifications than patients in the other registries (MTCCoRe, 28.6% T3-4 and 49.4% N1; TCR, 12.7% and 32.2%; CCR, 18.6% and 32.4%; and SEER, 24% and 37.8%; p < 0.0001). Prevalence of M1 disease was 10% in MTCCoRe, 11.9% in TCR, 14.1% in CCR, and 9.5% in SEER (p < 0.0001). In the MTCCoRe, 11.4% underwent systemic therapy (compared with 0.3% in TCR and 5.6% in CCR). Conclusions: The clinicodemographic profile of patients with MTC enrolled in a multi-institutional registry differs from those enrolled in population-based databases, with lower proportions of Hispanic and Black patients but additive data on treatment modalities. Moving forward, MTCCoRe and other registry and clinical trial enrollment efforts should intentionally include underrepresented groups via community engagement techniques, patient stakeholder involvement, and inclusion of languages other than English in study materials to yield more generalizable results and conclusions. [ABSTRACT FROM AUTHOR]

Our goal was to assess the impact of childhood/adolescent cancer history on overall survival (OS) and disease-specific survival (DSS) in patients with parotid mucoepidermoid carcinoma (MEC). Patients who underwent surgical treatment for primary parotid MEC and those with a second malignancy of parotid MEC were retrospectively identified from the Surveillance, Epidemiology, and End Results (SEER) database. The primary outcome variables were OS and DSS. The hazard ratios (HRs) of these survival rates associated with cancer history were analysed using Cox regression models. In total, 2681 patients were included, 263 of whom had a second malignancy. The 10-year OS rates in the primary (72%) and second malignancy groups (59%) were significantly different. Cox regression confirmed that a history of cancer tended to decrease OS (p = 0.062, HR: 1.28, 95% confidence interval: 0.99 to 1.64). Subgroup analyses showed that a history of solid tumour as opposed to haematological cancer predicted worse OS, with central nervous system tumours exhibiting a more significant influence than others (p = 0.030 vs p = 0.088). Cancer history was not related to DSS. A history of childhood/adolescent cancer negatively influenced the prognosis of patients with parotid MEC, and this effect was primarily driven by a history of solid malignancy. [ABSTRACT FROM AUTHOR]

Asian American, Native Hawaiian, and Pacific Islander (AANHPI) patient populations are often defined as one monolithic group in medical research despite cultural, socioeconomic, and clinical heterogeneity. Although the general AANHPI population is underrepresented in reception of postmastectomy breast reconstruction, existing literature has not characterized the disaggregation of such rates for AANHPI ethnic subgroups. Patients who underwent mastectomy were identified in the 2007 to 2020 registries within the Surveillance, Epidemiology and End Results database. Patients were stratified by race and ethnicity, and additional demographic and oncologic variables were collected. Multivariate binary logistic regression was conducted to assess for reception of postmastectomy immediate breast reconstruction (p < 0.05). Among 33,422 AANHPI patients who underwent mastectomy, South Asian patients were associated with the highest breast reconstruction rates (33%) and Melanesians with the lowest (15%). Overall, AANHPI patients were associated with a lower breast reconstruction rate than non-Hispanic Whites (27% vs. 35%; p < 0.001). This difference increased from 6.4% in 2007 to 10% in 2020. After controlling for demographic and oncologic covariates, all AANHPI ethnic subgroups predicted a lower likelihood of breast reconstruction than non-Hispanic Whites (p < 0.001). Odds ratios for reconstruction ranged from 0.17 [95% confidence interval (95% CI), 0.11–0.27] for Melanesian patients to 0.45 (95% CI, 0.42–0.48) for South Asian patients. Disparities in the receipt of immediate breast reconstruction exist within the AANHPI patient population in the United States. This analysis supported the need for disaggregation in plastic surgery research for improved knowledge and targeted interventions. [ABSTRACT FROM AUTHOR]

Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed. [ABSTRACT FROM AUTHOR]

Aims/Background We aimed to investigate the impact of postoperative chemotherapy (POCT) on survival in patients with primary central nervous system lymphoma (PCNSL) using data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods This study included 786 PCNSL patients, of which 605 received chemotherapy after surgery, and 181 did not. Data from the SEER registry database (2007–2020) were used to analyze PCNSL. Baseline information, including age, sex, race, marital status, primary tumour site, histological type, summary stage, surgical procedures, chemotherapy, and radiotherapy, was analyzed. Propensity Score Matching (PSM) (1:1) was employed to balance the effects of confounding variables between the two groups. Subsequently, Cox regression and bidirectional stepwise regression were used to identify independent prognostic factors. Kaplan-Meier (K-M) survival curves were constructed to assess the impact of POCT on patient prognosis. Additionally, two cases of PCNSL with typical magnetic resonance imaging appearances were presented. Results Multivariate Cox regression results revealed that age older than 60 years (hazard ratio [HR] = 1.786; 95% confidence interval [CI]: 1.272–2.509; p = 0.001) and absence of POCT (HR = 2.841; 95% CI: 2.159–3.738; p < 0.001) were independent prognostic risk factors, while primary tumour locations in the meninges (HR = 0.136; 95% CI: 0.032–0.569; p = 0.006) and other nervous system regions (HR = 0.552; 95% CI: 0.326–0.936; p = 0.027), as well as histological morphologies such as diffuse large B-cell lymphoma (HR = 0.233; 95% CI: 0.128–0.425; p < 0.001) and non-Hodgkin lymphoma (HR = 0.559; 95% CI: 0.356–0.876; p = 0.011), were associated with favourable patient outcomes. K-M curves demonstrated that the group undergoing POCT had a significantly more favourable prognosis compared to the non-POCT group, before (HR = 0.454; 95% CI: 0.343–0.600; p < 0.0001) or after PSM (HR = 0.580; 95% CI: 0.431–0.780; p < 0.0001). For patients with PCNSL, those with tumours located in the infratentorial region (HR = 0.231; 95% CI: 0.078–0.682; p = 0.046), supratentorial region (HR = 0.250; 95% CI: 0.163–0.383; p < 0.0001), overlapping brain regions (HR = 0.201; 95% CI: 0.056–0.727; p = 0.0058), and those who underwent biopsy (HR = 0.740; 95% CI: 0.463–1.182; p = 0.003), subtotal resection (STR) (HR = 0.490; 95% CI: 0.265–0.906; p = 0.0064), or gross total resection (GTR) (HR = 0.613; 95% CI: 0.292–1.287; p = 0.0003) had better prognoses in the postoperative chemotherapy group compared to the non-chemotherapy group. Conclusion POCT significantly improves the prognosis of PCNSL patients and identifies the characteristics of the benefiting population. This information aids clinical practitioners in designing personalized treatment plans for individuals and advancing precise treatment. [ABSTRACT FROM AUTHOR]

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node staging systems in DTC using a competing risks model. We used SEER database records (1998–2016) of 16,527 DTC patients, analyzing N stage, positive lymph node numbers (PLNNs), metastatic lymph node ratio (MLNR), log odds of positive lymph nodes (LODDS), and log odds of the negative lymph node (NLN)/T stage ratio (LONT). Univariate and multivariate analyses in a competing risks model were performed, along with subgroup analyses based on demographic and clinical characteristics. In this study of 16,527 patients with DTC, different lymph node staging systems showed different prognostic correlations in univariate and multivariate analyses. In particular, PLNNs showed significant prognostic correlations in several subgroups. Additionally, PLNNs were more suitable as a lymph node staging system for DTC than LODDS and MLNR in N1 stage subgroups, with an optimal cut-off of 13. Receiver operating characteristic curves, calibration curves and nomograms improved the clinical utility of the prognostic model based on PLNNs. Using competing risks model and subgroup analyses, we found that PLNNs had the best prognostic discriminatory efficacy for patients with DTC, especially those with N1 stage disease, and had an optimal cut-off value of 13. [ABSTRACT FROM AUTHOR]

Background: Colorectal adenosquamous carcinoma (ASC) is a rare subtype of colorectal carcinoma. This study presents findings from a large database query to highlight the demographic, clinical, and pathological factors, prognosis, and survival of colorectal ASC. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients with colorectal ASC diagnosed between 2000 and 2020 and assess factors associated with overall survival (OS) and cause-specific survival (CSS). Results: Among 284 identified cases, the median age of diagnosis was 64 years. The majority of patients were White (69.0%), with income ≤ $70,000 (62.3%), and lived in metropolitan areas (85.6%). Regarding tumor characteristics, the majority of tumors were poorly differentiated (49.6%), regional stage (39.8%), size of > 4.0 cm (41.5%), and had a negative lymph node status (47.2%). Primary sites were the rectum (35.2%) and colon (64.8%). In patients with primary site to the rectum, the majority of treatment modality was multimodal therapy (40.0%). The main treatment modality for the primary site to the colon was surgery only (46.2%), followed by surgery + chemotherapy (34.2%). The overall 5-year survival was 31.3 (95% C.I. 28.4–34.2) and the 5-year cause-specific survival (CSS) was 40.1% (95% C.I. 36.9–43.3). Multivariate analysis showed age ≥ 60 years, regional stage, and distant stage were negative prognostic factors. An income of > $70,000, multimodal therapy, and surgery with chemotherapy were positive prognostic factors. Conclusion: Colorectal adenosquamous carcinomas are more common in the non-Hispanic White populations and appear more frequently later in life (based on the median age of diagnosis at 64). Factors that contributed to a worse prognosis were an age of diagnosis ≥ 60 years, regional stage, and distant stage. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging. Methods: Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification. Results: While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata. Conclusions: Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer. [ABSTRACT FROM AUTHOR]

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Purpose: Adrenocortical carcinoma (ACC) is an uncommon adrenal gland endocrine tumor that has a poor prognosis in children. We aimed to conduct a population-based cohort study to predict overall survival (OS) in pediatric patients with ACC. Methods: We used the Surveillance, Epidemiology, and End Results (SEER) database to conduct a retrospective cohort research on pediatric patients diagnosed with ACC between 1975 and 2018. We examined demographic characteristics, tumor stage and size, treatment options, and survival results. Kaplane-Meier estimations were used to generate survival curves based on several parameters. To compare survival curves, the log-rank test was applied.Cox proportional-hazards regression was used to determine the variables related with OS. In addition, we created a nomogram to predict overall survival in pediatric ACC patients. Results: A total of 143 pediatric ACC patients were identified. Females were the most impacted (60.8%). Overall 1 year, 3 year, and 5 year survival rates were 75.0%, 57.6%, and 53.7% for all patients, respectively. In comparison to older patients (5–19 years), younger patients (≤ 4 years) were shown to have more positive characteristics, including a higher likelihood of local disease (29.4% vs. 14%, P < 0.001), tumors less than 10 cm (23.1% vs. 14.7%, P < 0.001), and improved overall survival (5 year OS 89.6% vs. 27.7%, P < 0.001). Age at diagnosis, SEER stage, and surgery were significant independent predictors of OS in this model, according to the results of Cox proportional hazard regression. After that, we developed a nomogram for predicting OS in children with ACC. Patients older than 4 years old had a higher chance of dying. Furthermore, the higher the SEER stage, the higher the risk of death. Patients who do not have surgery have a worse survival rate than those who do. Conclusions: Our study revealed that age at diagnosis, SEER stage, and surgery were found to be the most important predictors of the overall survival of pediatric ACC. These findings contribute to the existing body of knowledge and emphasize the importance of continued research to advance our understanding of pediatric ACC and improve patient care. [ABSTRACT FROM AUTHOR]

Introduction: Uterine carcinosarcoma is an aggressive gynecologic malignancy that accounts for 5% of all gynecological malignancies. There is a disproportion in its incidence and mortality among different races. This study describes demographic and clinicopathological factors and racial disparities affecting the survival of patients with uterine carcinosarcoma. Methods: Data on uterine carcinosarcoma patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2020. Results: Of the 11,338 patients identified, the median age at diagnosis was 68 years, and the five-year cause-specific survival (CSS) rate was 38.7%. for all races. Compared with Asian patients (39.5%, 95% CI, 36.0–43.4%), Hispanic patients (39.4%, 95% CI, 36.5–42.5%), and White patients (37.9%, 95% CI, 36.7–39.2%), Black patients accounted for 21% of the patients and had a significantly lower 5-year CSS (95% CI, 27.2–31.2%). The CSS rates were 84.4% (95% CI, 83.3–85.6%) for localized tumors, 68.5% (95% CI, 66.9–70.1%) for regional tumors, and 39.0% (95% CI, 36.9–41.2%) for distant tumors. Multimodal treatment involving chemotherapy, surgery, and radiation improved the overall one- and five-year survival rates by 88.2% (95% CI, 87.0–89.5%) and 52.8% (95% CI, 50.7–55.1%), respectively, across all disease stages. Multivariate analysis identified age >60 years, Black race, tumor size >4 cm, and distant metastases as independent risk factors for mortality (p < 0.0001). Conclusions: This large database study presents the most up-to-date epidemiological information regarding cases of uterine carcinosarcoma. The findings suggest that a combination of surgery, chemotherapy, and radiation may be most efficacious in treating this malignancy, especially in patients with distant disease. [ABSTRACT FROM AUTHOR]

Background: The prevalence of papillary thyroid cancer is gradually increasing and the trend of youthfulness is obvious. Some patients may not be able to undergo surgery, which is the mainstay of treatment, due to physical or financial reasons. Therefore, the prediction of cancer-specific survival (CSS) in patients with non-operated papillary thyroid cancer is necessary. Methods: Patients’ demographic and clinical information was extracted from the Surveillance, Epidemiology, and End Results database. SPSS software was used to perform Cox regression analyses as well as propensity score matching analyses. R software was used to construct and validate the nomogram. X-tile software was used to select the best cutoff point for patient risk stratification. Results: A total of 1319 patients were included in this retrospective study. After Cox regression analysis, age, grade, T stage, M stage, radiotherapy, and chemotherapy were used to construct the nomogram. C-index, calibration curves, and receiver operating characteristic curves all verified the high predictive accuracy of the nomogram. The decision curve analysis demonstrated that patients could gain clinical benefit from this predictive model. Survival curve analysis after propensity score matching demonstrated the positive effects of radiotherapy on CSS in non-operated patients. Conclusion: Our retrospective study successfully established a nomogram that accurately predicts CSS in patients with non-operated papillary thyroid cancer and demonstrated that radiotherapy for operated patients can still help improve prognosis. These findings can help clinicians make better choices. [ABSTRACT FROM AUTHOR]

Background: Comprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking. Patient and method: The surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration. Result: 577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM. Conclusion: The competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe. [ABSTRACT FROM AUTHOR]