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Objectives: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state‐based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state‐based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion's impact on staging, treatment, and mortality in cancer patients. Data sources: Cancer case data (2000–2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical). Study design: A Difference‐in‐Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster Bootstrap. Data collection: Data was accessed via SEER*Stat. Principal findings: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster Bootstrap techniques never rejected the null hypotheses. Conclusions: This study reiterates the importance of explicit inference. Future state‐based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy. [ABSTRACT FROM AUTHOR]

Background: This study aimed to examine clinicopathologic characteristics, treatment patterns, and survival rates in a contemporary population-based cohort of adult prostate sarcoma patients. Methods: In the Surveillance, Epidemiology, and End Results database (2004–2020), adult patients with prostate sarcoma were identified. Descriptive statistics, Kaplan–Meier analyses, smoothed cumulative incidence plots, and Cox regression models were used. Results: Of 125 patients, 45 (36%) harbored leiomyosarcoma, 17 (14%) had rhabdomyosarcoma, 15 (12%) had stromal sarcoma, 17 (14%) had sarcoma not otherwise specified (NOS), and 31 (25%) had other sarcoma subtypes. Metastatic stage was most common in the rhabdomyosarcoma patients (44%) and least common in the leiomyosarcoma (21%) and stromal sarcoma (20%) patients. Most of the rhabdomyosarcoma patients received the combination of systemic and radiation therapy with (24%) or without radical surgery (35%), whereas most of the leiomyosarcoma and stromal sarcoma patients underwent radical surgery with (22 and 13%) or without (22 and 47%) radiation. In the overall population, the median overall survival was 27 months. The 5-years overall versus cancer-specific versus other-cause mortality rates were respectively 71 versus 58 versus 13%. In the multivariable Cox regression models, the highest overall mortality was exhibited by the patients with metastatic disease (hazard ratio [HR] 2.87; 95% confidence interval [CI] 1.55–5.31; p < 0.001) or unknown disease stage (HR 2.94; 95% CI 2.20–7.21; p = 0.019). Conversely, of all the histologic subtypes, only stromal sarcoma distinguished itself by lower overall mortality (HR 0.41; 95% CI 0.18–0.96; p = 0.039). Conclusions: Four major histologic subtypes were identified. Among most adult sarcoma patients, treatment patterns vary according to histology, from multimodal therapy to radical prostatectomy alone. These treatment differences reflect equally important heterogeneity in survival patterns. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint inhibitors (ICIs) greatly improved outcomes of stage IV non-small cell lung cancer (NSCLC) in randomized clinical trials. Limited data exists regarding the survival improvement of ICI use at the population level. Methods: Clinical data of patients with pathologically confirmed stage IV NSCLC diagnosed in 2013 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival outcomes were compared before and after ICI use to assess the survival improvement in the immunotherapy era in the real world. Results: A total of 19,433 patients with stage IV NSCLC were included. Being female, age < 60 years, of American Indian and Asian or Pacific Islander race, married, non-squamous histology, without bone, brain, or liver metastases, and receiving chemotherapy were significantly associated with better prognosis in multivariable analyses. Propensity score matching (PSM) based on associated factors resulted in 8743 patients each in the non-immunotherapy and immunotherapy groups (1:1 ratio). After PSM, both overall survival (OS) (p < 0.001) and cancer-specific survival (CSS) (p < 0.001) were significantly improved in the immunotherapy group. The median OS (mOS) was 6.0 months in the non-immunotherapy group and 8.0 months in the immunotherapy group. The 1-, 2-, and 3-year OS rate was 29.0%, 14.2%, and 8.5% in the non-immunotherapy group, and 37.8%, 23.5%, and 16.7% in the immunotherapy group, respectively. Patients who were male, under 60 years old, married, white, had adenocarcinoma, had no bone or liver metastases, and received chemotherapy or radiation therapy were more likely to benefit from immunotherapy. Conclusions: Survival outcomes of patients with stage IV NSCLC were significantly improved in the immunotherapy era. Population-level benefits of survival varied among subgroups, and not all the increase in OS meant a clinically meaningful benefit. [ABSTRACT FROM AUTHOR]

Background: Colorectal leiomyosarcoma (CR-LMS) is a rare neoplasm arising from smooth muscle cells. It accounts for less than 0.1% of all colorectal malignancies. In this population-based study, we aim to understand the demographics, treatment characteristics, and pathologic factors associated with survival in CR-LMS. Methods: Data from the SEER Program (2000–2018) were analyzed using SEER*Stat and SPSS. Statistical methods included descriptive analysis, Kaplan–Meier survival curves, log-rank tests, and Cox proportional hazards regression to assess the impact of various factors on disease-specific and overall survival. Results: A total of 191 cases of CR-LMS were identified. Most patients were 60–69 years of age (median: 64 years) and Caucasian (78%). There was nearly the same distribution in sex (M:F ratio; 1:1.2). The overall 5-year observed survival was 50.3% (95% C.I., 46.3—54.2). The 5-year disease-specific survival (DSS) was 66.1% (95% C.I., 62.0—70.1). The 5-year overall survival after resection was 60.8% (95% C.I., 56.3—65.3). Multivariable analysis identified grades III and IV (p = 0.028) as negative predictors of overall survival. Regional spread and distant stage are negative predictors of overall survival (p < 0.01). Conclusion: Our data reveals that colorectal leiomyosarcoma (CR-LMS) often presents in patients around 64 years old with advanced stages and poor differentiation. Key adverse prognostic factors include older age, high tumor grade, large tumor size, and distant metastases, with surgical resection showing the best survival outcomes. To improve outcomes, further research and consolidation of data are essential for developing targeted therapies and comprehensive guidelines. [ABSTRACT FROM AUTHOR]

Purpose: Treatment for advanced gallbladder cancer (GBC) remains controversial, with various recommendations regarding the choice and combination of surgery and adjuvant therapy. The present article is targeting for the exploration of optimal treatment models for advanced GBC. Methods: AJCC (American Joint Committee on Cancer, 8th edition) stage III and stage IV GBC, were defined as advanced GBC. Patients with advanced GBC were identified using the Surveillance, Epidemiology, and End Results (SEER) database and departmental cohort. Because of the most representative, only gallbladder adenocarcinoma (GBAC) patients were selected. Based on their surgical status (No, Non-radical and Radical surgery), chemotherapy status (Chemotherapy, No chemotherapy), and radiotherapy status (Radiotherapy, No radiotherapy), treatment models were categorized. For the purposes of evaluating the treatment outcomes of various treatment models and determining the risk element for cancer-specific survival (CSS), Cox regression analysis was applied. Kaplan-Meier curves were used before and after adjusting for covariates, with log-rank tests used to analyze discrepancies between curves. Immunotherapy was analyzed using clinical data from departmental cohort. Finally, to compensate for the limitations of the database, a review examines the progress in treatment models for advanced GBC. Results: 5,154 patients aged over 18 years with solitary primary advanced GBC were identified from the SEER database. In advanced GBC patients, the treatment model has emerged as a significant prognostic factor. "Radical surgery + Chemotherapy + Radiotherapy" models maximally improved the CSS of advanced GBC before and after adjusting for covariates, while "No surgery + No chemotherapy + No radiotherapy" model had the lowest CSS. The present conclusions were supported even after subgroup analysis by AJCC stage. The efficacy of immunotherapy was demonstrated in the departmental cohort analysis. Additionally, this article provides a comprehensive overview of recent advancements in various emerging treatment strategies. Conclusion: Even when optimal treatment model cannot be pursued, providing comprehensive combinations of treatments to advanced GBC patients whenever possible is always beneficial for their survival. [ABSTRACT FROM AUTHOR]

Purpose: The optimal treatment of ypT1 rectal cancer after neoadjuvant chemoradiotherapy (nCRT) remains controversial. This study aimed to determine whether local excision is non-inferior to radical surgery and whether adjuvant chemotherapy (ACT) would improve survival in patients with ypT1 rectal cancer after nCRT. Methods: We enrolled 1212 and 91 patients with ypT1 rectal cancer underwent nCRT followed by radical surgery from the SEER database (2004–2018) and the Zhejiang Cancer Hospital (ZJCH) (2010–2022), respectively. Another 62 patients underwent LE were also identified from SEER registries. Propensity score matching was performed to balance baseline characteristics between patients in different treatment groups. Results: Regional nodal metastasis was histopathologically detected in 257 patients (20.7%) within the SEER cohort, showing a significant association with poor cancer-specific survival (CSS) and overall survival (OS). Consistent findings were also observed in the ZJCH cohort. After 1:1 propensity score matching (60 pairs), no significant differences were observed between the extended resection and local excision groups in CSS (hazard ratio [HR] 0.88, P = 0.785) and OS (HR 0.81, P = 0.450). Patients with regional nodal metastases were more likely to receive ACT, while no apparent survival benefit was observed with additional ACT after PSM adjusting (187 pairs). Notwithstanding, for individuals younger than 50 years, ACT might provide a survival benefit in CSS (HR 0.25, P = 0.033) and OS (HR 0.30, P = 0.022). Conclusion: Although patients with ypT1 rectal cancer have a non-negligible risk for nodal metastasis, oncologic outcomes of local excision following nCRT seem to be comparable to radical surgery. ACT could not effectively improve prognosis in patients with ypT1 tumors, except for those younger than 50 years of age. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study is to identify risk factors for the prognosis and survival of synchronous colorectal cancer and to create and validate a functional Nomogram for predicting cancer-specific survival in patients with synchronous colorectal cancer. Methods: Synchronous colorectal cancers cases were retrieved from the Surveillance, Epidemiology, and End Results database retrospectively, then they were randomly divided into training (n = 3371) and internal validation (n = 1440) sets, and a set of 100 patients from our group was used as external validation. Risk factors for synchronous colorectal cancer were determined using univariate and multivariate Cox regression analyses, and two Nomograms were established to forecast the overall survival and cancer-specific survival, respectively. We assessed the Nomogram performance in terms of discrimination and calibration. Bootstrap resampling was used as an internal verification method, and we select external data from our hospital as independent validation sets. Results: Two Nomograms are established to predict the overall survival and cancer-specific survival. In OS Nomogram, sex, age, marital status, ttumor pathological grade, AJCC TNM stage, preoperative serum CEA level, LODDS, radiotherapy and chemotherapy were determined as prognostic factors. In CSS Nomogram, age and marital status, AJCC TNM stage, tumor pathological grade, preoperative serum CEA level, LODDS, radiotherapy and chemotherapy were determined as prognostic factors.The C-indexes for the forecast of overall survival were 0.70, and the C-index was 0.68 for the training and internal validation cohort, respectively. The C-indexes for the forecast of cancer-specific survival were 0.75, and the C-index was 0.74 for the training and internal validation cohort, respectively. The Nomogram calibration curves showed no significant deviation from the reference line, indicating a good level of calibration. Both C-index and calibration curves indicated noticeable performance of newly established Nomograms. Conclusions: Those Nomograms with risk rating system can identify high risk patients who require more aggressive therapeutic intervention and longer and more frequent follow-up scheme, demonstrated prognostic efficiency. [ABSTRACT FROM AUTHOR]

Background: Nowadays, the number of oral cancer survivors is increasing, emphasizing the importance of thoroughly understanding diverse causes of death in oral cancer survivors. Our study aimed to investigate the distribution of causes of death after oral cancer diagnosis. Methods: Eligible patients were identified between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. We calculated the number of deaths in different demographic and clinicopathological variables during each follow-up period. Standardized mortality ratios(SMRs) were generated for each cause of death after oral cancer diagnosis. Results: A total of 30538 patients diagnosed with oral cancer were included, and 17654 deaths were reported during follow-up period. 27.08% of deaths were caused by non-caner reasons. The proportion of non-cancer related deaths increased with the extension of survival time, and non-cancer death accounted for 57.93% of all deaths when followed up more than 10 years. The most common non-cancer cause of death was cardiovascular disease (SMR 4.68#; 95%CI 4.46-4.92). Conclusions: Non-cancer causes of death should not be ignored in oral cancer patients. For oral cancer survivors, multidisciplinary follow-up strategy should be recommended to achieve longer survival time. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to construct two nomograms to predict all-cause mortality (ACM) and cause-specific mortality (CSM) in non-muscle-invasive bladder cancer (NMIBC) patients after transurethral resection of bladder tumors (TURBTs). We selected NMIBC patients who underwent TURBT between 2004 and 2017 from the Surveillance, Epidemiology, and End Results database. The patients were randomly divided into a training set and a validation set at a ratio of 7:3. The independent influencing factors of ACM and CSM in the training set were determined by univariate and multivariate Cox regression analyses. We then integrated those independent influencing factors to construct nomograms. These prediction nomograms were further verified in the validation set. The C-index, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve were used to evaluate the identification, calibration, predictive ability and clinical effectiveness of the nomograms. A total of 28,086 cases were ultimately included in this study, which were divided into a training set (19,661 individuals) and a validation set (8425 individuals). Nine variables, including age at diagnosis, race, marital status, tumor grade, T stage, tumor size, number of tumors, and primary site, were obtained via multivariate Cox regression of the training set and used to construct two nomograms prediction model. The C-index values for the ACM nomogram were 0.743 and 0.741 for the training and validation sets, respectively. Moreover, the corresponding values of the C-index for the CSM nomogram were 0.785 and 0.786, respectively. The ROC curves, calibration curves, and DCA curves showed good predictive performance. The nomograms can assist clinicians in identifying high-risk populations and devising more individualized treatment strategies for NMIBC patients. [ABSTRACT FROM AUTHOR]

Background: To identify the impact of surgical compliance on survival in patients with kidney cancer and to explore the factors that influence surgical compliance. Methods: Clinical date of kidney cancer patients were identified from the SEER databases, and the patients were divided into surgical compliance group and surgical noncompliance group. Cox survival analysis and Kaplan–Meier curves were used to evaluate the effect of surgical compliance on overall survival (OS) and cancer-specific survival (CSS). A diagnostic nomogram was constructed to quantify individual differences in compliance, and receiver operating characteristics (ROC) curves and calibration curves were used to assess the accuracy of the nomogram. Propensity score matching (PSM) was performed to balance potential baseline confounding factors. Results: Of the 133,950 patients eligible for surgical resection, 2,814 (2.1%) patients did not opt for surgery ultimately. Surgical noncompliance was associated with poor prognosis. In all patients, Cox regression analysis showed that surgical noncompliance was an independent predictor for OS [before: HR = 2.490, 95% CI 2.374–2.612, p < 0.001; after: HR = 2.380, 95% CI 2.202–2.573, p < 0.001] and CSS [before: HR = 2.490, 95% CI 2.318–2.675, p < 0.001; after: HR = 2.035, 95% CI 1.813–2.285, p < 0.001] of kidney cancer patients before and after PSM. Multivariable logistic regression revealed that older age, afro-american origin, lower household income, and advanced tumor grade were associated with surgical noncompliance. The ROC and calibration curves showed that the diagnostic nomogram had high predictive accuracy. Conclusion: Surgical compliance was an independent prognostic factor for OS and CSS in patients with kidney cancer, and surgical noncompliance was associated with poor survival. [ABSTRACT FROM AUTHOR]

The overall survival (OS) improvement after the advent of several novel systemic therapies, designed for treatment of metastatic urothelial carcinoma of the urinary bladder (mUCUB), is not conclusively studied in either contemporary UCUB patients and/or non‐UCUB patients. Within the Surveillance, Epidemiology, and End Results database, contemporary (2017–2020) and historical (2000–2016) systemic therapy‐exposed metastatic UCUB and, subsequently, non‐UCUB patients were identified. Separate Kaplan–Meier and multivariable Cox regression (CRM) analyses first addressed OS in mUCUB and, subsequently, in metastatic non‐UCUB (mn‐UCUB). Of 3443 systemic therapy‐exposed patients, 2725 (79%) harbored mUCUB versus 709 (21%) harbored mn‐UCUB. Of 2725 mUCUB patients, 582 (21%) were contemporary (2017–2020) versus 2143 (79%) were historical (2000–2016). In mUCUB, median OS was 11 months in contemporary versus 8 months in historical patients (Δ = 3 months; p <.0001). After multivariable CRM, contemporary membership status (2017–2020) independently predicted lower overall mortality (OM; hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.60–0.76; p <.001). Of 709 mn‐UCUB patients, 167 (24%) were contemporary (2017–2020) and 542 (76%) were historical (2000–2016). In mn‐UCUB, median OS was 8 months in contemporary versus 7 months in historical patients (Δ = 1 month; p =.034). After multivariable CRM, contemporary membership status (2017–2020) was associated with HR of 0.81 (95% CI = 0.66–1.01; p =.06). In conclusion, contemporary systemic therapy‐exposed metastatic patients exhibited better OS in UCUB. However, the magnitude of survival benefit was threefold higher in mUCUB and approximated the survival benefits recorded in prospective randomized trials of novel systemic therapies. [ABSTRACT FROM AUTHOR]

Background: This study aimed to predict metastasis risk and identify prognostic factors of gastrointestinal signet ring cell carcinoma (SRCC) using data from the SEER database, the largest cancer dataset in North America. Methods: Data were obtained from the SEER database, covering 17 cancer registries from 2004 to 2020. Demographic and clinical data included sex, age, race, tumor location, size, pathological grade, stage, overall survival time, and treatment modalities. Statistical analyses were conducted using SPSS and R software. Propensity Score Matching (PSM) ensured comparable baseline characteristics between gastric cancer (GC) and colorectal cancer (CRC) groups. LASSO regression analysis identified predictors of metastasis, leading to the construction of predictive models using the lrm function in R. Nomograms were visualized with the "rms" package and assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Cox regression analyses identified prognostic indicators for overall survival (OS), and Kaplan–Meier curves compared OS between high-risk and low-risk groups. Results: From 2004 to 2020, 7680 SRCC patients were identified, including 4980 GC and 2700 CRC patients. CRC patients were older and had larger tumors, higher staging, and worse differentiation. Nomograms demonstrated good discriminative ability, with AUCs of 0.704 and 0.694 for GC, and 0.694 and 0.701 for CRC in training and validation cohorts, respectively. The DCA curve indicates that this predictive model has a high gain in predicting metastasis and OS. Conclusions: The nomograms effectively predicted metastasis risk and OS in metastatic SRCC patients, offering clinical utility in stratifying patients and guiding treatment decisions, thereby enhancing personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify the risk factors associated with ampullary duodenal carcinoma (a-DC) and develop a clinical model to dynamically and accurately predict the risk of lymph node metastasis (LNM) in a-DC patients. Methods: Data from 4077 patients (2004–2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010–2020) from Zhejiang Provincial People's Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram's accuracy and generalizability were externally validated.. Results: After screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P < 0.001), tumor size (P < 0.001), grade (P < 0.001), and tumor extension (P < 0.001). The area under the curve of the nomogram was 74.8% in the training group and 88.9% in the external validation group. The calibration curves demonstrated that the LNM predictions made by the nomogram were in satisfactory agreement with the actual observed LNM. Additionally, the decision curve analysis curves indicated effective clinical utility of the nomogram. Conclusions: A nomogram based on the LASSO-logistic analysis was constructed to predict a-DC LNM, demonstrating good performance and clinical application value. [ABSTRACT FROM AUTHOR]

Background: Difficulty in achieving complete resection leads to a poor prognosis for retroperitoneal soft tissue sarcoma, hence emphasizing the significance of adjuvant treatment. The benefit of preoperative radiotherapy for retroperitoneal liposarcoma was initially demonstrated by the STRASS trial. However, the impact of intraoperative radiotherapy (IORT) on retroperitoneal liposarcoma remains unexplored. Method: Patients with retroperitoneal liposarcoma were identified in the Surveillance, Epidemiology, and End Results (SEER) database, treated between 2000 and 2019. Subsequently, a 1:1 propensity score-matched (PSM) analysis was conducted based on variables identified from a multivariate analysis. T-tests were used to assess differences in normally distributed continuous variables, while the rank-sum test was applied to variables that did not follow a normal distribution. The chi-squared test was utilized to evaluate differences in categorical variables. Ultimately, survival analysis was performed using SPSS to evaluate patient prognosis. Result: A total of 2129 patients with retroperitoneal liposarcoma were included in our study. Age, sex, histology, grading, chemotherapy, and tumor size as independent prognostic risk factors for these patients through multivariate Cox regression analysis. Subsequently, 66 patients were included in the survival analysis through PSM, with 33 patients receiving IORT. Finally, the survival analysis revealed that there was no difference in overall survival among patients with retroperitoneal liposarcoma, regardless of whether they received IORT or not (p= 0.711). Conclusion: As an exploratory study, our findings suggest that patients may not derive benefit from intraoperative radiotherapy. These observations are intended to lay the groundwork for future prospective clinical studies. [ABSTRACT FROM AUTHOR]

Background: Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk. Methods: Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer. Results: Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model's area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach. Conclusion: We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans. [ABSTRACT FROM AUTHOR]

Purposes: Whether patients with early-onset colorectal cancer (EOCRC) receive chemotherapy has been controversial, so our study aimed to screen patients with EOCRC who benefit from chemotherapy. Methods: A total of 2166 EOCRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into chemotherapy and non-chemotherapy groups, propensity score matching (PSM) was performed to balance the differences between the groups, and the Kaplan–Meier method was used to calculate the cancer-specific survival (CSS) of EOCRC patients. Multifactorial COX regression analysis was used to identify independent prognostic factors for CSS and to construct a nomogram for predicting CSS in EOCRC patients. The overall risk score was calculated based on Nomogram, and EOCRC patients were classified into high-risk and low-risk groups to assess further chemotherapy's therapeutic effect on patients with different risk stratification. Results: Before PSM, patients in the chemotherapy group had poorer CSS (p < 0.001). After PSM, there was no significant difference in patient CSS between the two groups (p = 0.057). Independent prognostic factors (Race, Grade, Pathology, AJCC.N, AJCC.M, CEA, Marital. Status) were screened according to multifactorial COX regression analyses and included in the Nomogram predicting CSS in EOCRC patients. A risk stratification system for EOCRC patients was further developed, and the results showed that chemotherapy had no significant effect on CSS in the low-risk group of patients, but in the high-risk group, chemotherapy significantly improved CSS in EOCRC patients. Conclusions: We developed a clinical risk model by combining different risk factors, which can accurately screen those with high-risk EOCRC for benefit from chemotherapy. For low-risk EOCRC patients, our results did not observe a better survival benefit from chemotherapy, and more prospective studies are needed in the future to prove our conclusions. [ABSTRACT FROM AUTHOR]

Background: Limited information is available regarding the relative risks of cancer-specific mortality and noncancer-specific mortality among long-term survivors with differentiated thyroid cancer (DTC). Methods: In this retrospective study, nationwide data from the Surveillance, Epidemiology, and End Results database (1992-2020) were utilized. The Accelerated Failure Time Model was applied to calculate Survival Time Ratios (TR), with the primary focus on mortality resulting from DTC. The competing risks model was employed to investigate the relative risks of various outcomes in DTC patients with a survival duration of 5 years or more. Results: In our cohort, 279 patients succumbed to DTC, while 748 died from other diseases. Notably, in DTC cohorts, noncancer-specific mortality rates were consistently higher than DTC-specific mortality rates across different age groups and genders. The risk of DTC and noncancer-specific mortality varied based on the TNM stage. With more advanced disease stages, the risk of DTC and other cancer-specific mortality gradually increased. The cumulative mortality from other cancer-specific causes was consistently the lowest. Conclusions: In long-term surviving DTC patients, noncancer-specific mortality outweighed DTC-specific mortality irrespective of age and gender. For stage I and II patients, increased attention should be directed toward noncancer-specific mortality in postoperative follow-ups. Conversely, for stage III and IV patients, greater consideration should be given to DTC-related causes of death. In addition, for stage III and IV DTC patients, the risk of death from other cancers was significantly higher than for stages I and II. [ABSTRACT FROM AUTHOR]

Purpose: The overall survival of patients with pancreatic cancer is extremely low. We aimed to establish machine learning (ML) based model to accurately predict three-year survival and prognosis of pancreatic cancer patients. Methods: We analyzed pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic analysis were employed to select variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were evaluated by area under the curve (AUC), area under precision-recall curve (PRAUC), accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores and Balanced Accuracy (bacc) and F Beta Score (fbeta). An optimal ML model was constructed to predict three-year survival, and the predictive results were explained by SHapley Additive exPlanations (SHAP) framework. Meanwhile, 101 ML algorithm combinations were developed to select the best model with highest C-index to predict prognosis of pancreatic cancer patients. Results: A total of 20,064 pancreatic cancer patients from SEER database was consecutively enrolled. We utilized eight clinical variables to establish prediction model for three-year survival. CatBoost model was selected as the best prediction model, and AUC was 0.932 [0.924, 0.939], 0.899 [0.873, 0.934] and 0.826 [0.735, 0.919] in training, internal test and external test sets, with 0.839 [0.831, 0.847] accuracy, 0.872 [0.858, 0.887] sensitivity, 0.803 [0.784, 0.825] specificity and 0.832 [0.821, 0.853] precision. Surgery type had the greatest effects on three-year survival according to SHAP results. For prognosis prediction, "RSF+GBM" algorithm was the best prognostic model with C-index of 0.774, 0.722 and 0.674 in training, internal test and external test sets. Conclusions: Our ML models demonstrate excellent accuracy and reliability, offering more precise personalized prognostic prediction to pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Background: Epithelioid hemangioendothelioma (EHE), is an uncommon, intermediate-grade malignant vascular tumor that can manifest in diverse organs, including the liver, lungs, and bones. Given its unique malignancy profile and rarity, there lacks a consensus on a standardized treatment protocol for EHE, particularly for hepatic epithelioid hemangioendothelioma (HEHE). This study aims to elucidate factors influencing the clinical prognosis of EHE by analyzing data from the SEER database, complemented with insights from a departmental cohort of 9 HEHE cases. Through this, we hope to shed light on potential clinical outcomes and therapeutic strategies for HEHE. Methods: Using SEER data from 22 registries, we analyzed 313 liver cancer patients with ICD-O-3 9130 and 9133 histology. Twelve variables were examined using Cox regression and mlr3 machine learning. Significant variables were identified and compared. Clinical data, imaging characteristics, and treatment methods of nine patients from our cohort were also presented. Result: In univariate and multivariate Cox regression analyses, Age, Sex, Year of diagnosis, Surgery of primary site, Chemotherapy, and Median household income were closely related to survival outcomes. Among the ten survival-related machine learning models, CoxPH, Flexible, Mboost, and Gamboost stood out based on Area Under the Curve(AUC), Decision Curve Analysis(DCA), and Calibration Curve Metrics. In the feature importance analysis of these four selected models, Age and Surgery of primary site were consistently identified as the most critical factors influencing prognosis. Additionally, the clinical data of nine patients from our cohort not only demonstrated unique imaging characteristics of HEHE but also underscored the importance of surgical intervention. Conclusion: For patients with resectable HEHE, surgical treatment is currently a highly important therapeutic approach. [ABSTRACT FROM AUTHOR]

Despite radiotherapy (RT) is recognized as preferred initial therapy for early-stage low-grade follicular lymphoma (FL) by many international practice guidelines, the medical oncologist has improperly underutilized RT, and diverse management strategies, including systemic therapy (ST), combined modality (CM) and watch and wait (WW), are still used. Except survival outcomes, previous studies concerned little about the treatment-related toxicity, which is also important factor in choosing initial management strategy, especially second primary malignancies (SPMs). The aim of this study was to compare the overall survival (OS) and the SPMs risk between different management strategies, which can provide guidance for the choice of optimal initial management strategy. Data was acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Finally, A total 10,900 patients were identified, in which 930 cases developed SPMs. The use of radiotherapy (RT) has remained consistently low, with a utilization rate of around 20%, while most patients have received watchful waiting (WW) and systemic therapy (ST). In the rituximab era, multivariate analysis indicated that RT exhibited significantly superior OS and did not increase SPMs risk in comparison with ST and WW. At the same time, although there were no significant differences in OS between CM and RT, RT had significantly lower SPMs risk in comparison with CM. The use of RT improved the OS and did not increase the SPMs risk in comparison with other management strategies. Considering the low application rate of RT, oncologists should emphasize and increase the use of RT as an initial management strategy in patients with early-stage low-grade FL. [ABSTRACT FROM AUTHOR]

Although the recent drop in melanoma mortality has been attributed to the introduction of newer therapies, the impact of ongoing public efforts remains unknown. Characterize and model melanoma mortality trends before the era of molecular and immune therapies (1969-2014) in the U.S. and Australia. Differential time series analysis based on population-ascertained melanoma mortality rates from the U.S. and Australia. Mortality rates were modeled and compared to the trajectories of ten other cancers. Melanoma mortality rates have been significantly decelerating since the 1970s in both the U.S. (P <.0001) and Australia (P =.0021). Zero acceleration occurred around 2001 (95% CI: 1996, 2008) for the U.S. and 2004 (95% CI: 1999, 2011) for Australia. Male mortality rates decelerated 3x-4x faster than females in both countries. Melanoma mortality followed a similar quadratic function (R2 > 0.9) to 10 other cancers, albeit with a later inflection point (1986 vs 2001) and broader focal width. Absolute mortality data used without further stratification or considering cancer incidence or covariates. Melanoma deaths have been decelerating for the past 5 decades, reaching an inflection point around 2001, suggesting that mitigating campaigns were already afoot in both the U.S. and Australia before the advent of modern therapies. [ABSTRACT FROM AUTHOR]

Nationwide datasets are frequently used to examine cancer trends and outcomes in the U.S. Understanding the strengths and limitations of the commonly used Surveillance, Epidemiology, and End Results (SEER) Program and the National Cancer Database (NCDB) is important when designing studies and interpreting results. We used colorectal cancer (CRC) as a case study to compare information available. We identified 575,128 (SEER) and 1,578,046 (NCDB) adults diagnosed with CRC between 2004 and 2021. The distribution of age, tumor location, stage, and treatment did not meaningfully differ between SEER and NCDB. SEER represents racially and ethnically diverse populations, including a higher proportion of Hispanic (11.7% vs 5.8%) and Asian/Pacific Islander (8.6% vs 3.3%) persons. SEER includes more information on area-level characteristics, such as county-level measures of poverty, unemployment, and migration and census tract-level measures of socioeconomic status. Age-adjusted incidence, mortality rates, and cause-specific survival are only available in SEER, facilitating detailed analyses of racial, ethnic, and socioeconomic differences in cancer incidence and mortality. NCDB provides information on tumor characteristics and treatment not available in SEER, including microsatellite instability, KRAS mutation, palliative treatment, unplanned readmissions, and 30-day mortality after surgery, facilitating analyses of treatment effectiveness and outcomes. Five-year overall survival was similar in SEER (55.6%) vs NCDB (57.5%). [ABSTRACT FROM AUTHOR]

Background: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. Methods: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). Results: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan–Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44–0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19–12.84]). Conclusions: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans. [ABSTRACT FROM AUTHOR]

We aimed to investigate the risk of bladder cancer (BCa) survivors developing or dying from 15 specific-subsequent primary cancers (SPCs). A total of 229,554 BCa survivors were identified from the Surveillance, Epidemiology, and End Results database. Incidence and mortality per 10,000 person-years, absolute excess risk (AER) per 10,000 person-years, standardized incidence ratios, and standardized mortality ratios were calculated. Among BCa survivors, 38,207 developed SPCs and 17,546 died of SPCs. The risk of developing and dying from SPCs was significantly high for 10 and 6 of the 12 common SPCs in men, respectively, while for 6 and 5 of the 14 common SPCs in women, respectively. The SPCs with high risk of development in men were colorectal, breast, liver, and pancreatic cancer, and the ones with high risk of death were liver and pancreatic cancer. Moreover, SPCs with a high risk of development or death among young BCa survivors include ureter, kidney and renal pelvis, and lung cancer. In addition, BCa survivors within 1 year of diagnosis have a significantly higher risk of development and death from ureter, kidney and renal pelvis, prostate, and cervix cancer, but a lower risk of prostate cancer than the general population after 5 years of diagnosis. Lung cancer had a significantly high risk of development but a low risk of death. Among BCa survivors, the risk of developing or dying from few SPCs is significantly high. These findings may provide an important basis for clinical follow-up of BCa survivors. [ABSTRACT FROM AUTHOR]

Background: Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice. Methods: Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation. Results: A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability. Conclusion: In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis. [ABSTRACT FROM AUTHOR]

Pilocytic astrocytoma (PA) is classified as a Grade I benign neuroglial tumor. The extent of surgical resection is a critical factor influencing the prognosis for patients with PA. In prior researches of PA, the extent of surgical resection is generally categorized into GTR, STR and biopsy. In some researches on brain tumor surgeries, the extent of resection also includes GTL. There is no existing research specifically comparing the efficacy of GTR versus GTL in PA treatment. In this study, the data we used are from the SEER database. We categorized the extent of resection into GTL, GTR, STL, STR, biopsy, and no surgery based on SEER classification of surgical procedures, to investigate the impact of extent of resection on PA patient survival. A multivariate logistic regression model was utilized to acquire odds ratios (OR) for different extent of resection. Survival outcomes across different extent of resection (GTL, GTR, STL, STR, biopsy, no surgery) were assessed using Kaplan–Meier survival curve analysis, with curve comparisons conducted via log-rank tests. The impact of various risk factors on survival was assessed using the Cox proportional hazards model. The hazard ratio (HR) was employed to quantify the influence of one or more factors on overall survival throughout the follow-up period. Multivariate Cox analysis revealed that age, tumor location, extent of resection, as well as the application of radiotherapy and chemotherapy, all significantly impacted prognosis. Compared to GTL, GTR did not significantly increase the risk of mortality (HR 1.17; 95% CI 0.73–1.86, p = 0.5). Furthermore, there was no statistically significant difference between the Kaplan–Meier survival curves of the two groups (p = 0.18). We employed propensity score matching (PSM) to balance the differences in baseline characteristics of patients receiving chemotherapy or radiotherapy. A total of 4429 patients were included in this study. Age, diagnosis period, race, tumor size, and tumor location as influential on the extent of resection. Age, tumor location, extent of resection, and application of radiotherapy and chemotherapy influenced the survival of PA patients. The Kaplan–Meier survival curves revealed that the long-term survival rate for GTR is slightly higher than that for GTL. The PSM analysis revealed that the application of radiotherapy and chemotherapy was associated with the reduction of overall survival in PA patients. In conclusion, there was no significant difference in survival between GTR and GTL, so GTR with less damage was preferred. The application of radiotherapy and chemotherapy can reduce overall survival of patients with PA. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma multiforme (GBM) with synchronous metastasis(SM) is a rare occurrence. We extracted the data of GBM patients from the SEER database to look into the incidence of SM in GBM, determine the prognostic significance of SM in GBM, and assess therapeutic options for patients presenting with SM. Methods: From 2004 to 2015, information on GBM patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was employed to mitigate confounding factors between SM and non-SM groups, subsequently investigating the prognostic significance of SM in patients with GBM. Multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic variables for GBM patients with SM. A forest plot was used to visualize the results. Results: A cohort of 19,708 patients was obtained from the database, among which 272 (1.4%) had SM at the time of diagnosis. Following PSM at a 3:1 ratio, in both univariate and multivariate cox regression analysis, SM (HR = 1.27, 95% CI: 1.09-1.46) was found to be an independent predictive predictor for GBM patients. Furthermore, the Cox proportional hazard forest plot demonstrated that independent risk variables for GBM patients with SM included age (Old vs. Young, HR = 1.44, 95% CI: 1.11-1.88), surgery (biopsy vs. no surgery, HR = 0.67, 95% CI: 0.46-0.96;Subtotal resection vs. no surgery, HR = 0.47, 95% CI: 0.32-0.68;Gross total resection vs. no surgery, HR = 0.44, 95% CI: 0.31-0.62), radiotherapy (HR = 0.58, 95% CI: 0.41-0.83), and chemotherapy (HR = 0.51, 95% CI: 0.36-0.72). Conclusion: The predictive value of SM in GBM was determined by this propensity-matched analysis using data from the SEER database. Radiotherapy, chemotherapy, and surgery constitute an effective treatment regimen for patients with SM. A more positive approach toward the use of aggressive treatment for GBM patients with SM may be warranted. [ABSTRACT FROM AUTHOR]

Background: The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients. Methods: Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform. Results: We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (https://wubopredict.shinyapps.io/dynnomapp/). Conclusion: The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic ductal adenocarcinoma (PDAC) can be classified into distinct histological subtypes based on the WHO nomenclature. The aim of this study was to compare the prognosis of conventional PDAC (cPDAC) against the other histological variants at the population level. Methods: The Surveillance, Epidemiology and End Results (SEER) database was used to identify patients with microscopically confirmed PDAC. These patients were divided into 9 histological subgroups. Overall survival was assessed using the Kaplan-Meier method and Cox regression models stratified by tumor histology. Results: A total of 159,548 patients with PDAC were identified, of whom 95.9% had cPDAC, followed by colloid carcinoma (CC) (2.6%), adenosquamous carcinoma (ASqC) (0.8%), signet ring cell carcinoma (SRCC) (0.5%), undifferentiated carcinoma (UC) (0.1%), undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) (0.1%), hepatoid carcinoma (HC) (0.01%), medullary carcinoma of the pancreas (MCP) (0.006%) and pancreatic undifferentiated carcinoma with rhabdoid phenotype (PUCR) (0.003%). Kaplan-Meier curves showed that PUCR had the worst prognosis (median survival: 2 months; 5-year survival: 0%), while MCP had the best prognosis (median survival: 41 months; 5-year survival: 33.3%). In a multivariable Cox model, several histological subtypes (i.e. CC, ASqC, SRCC, UCOGC) were identified as independent predictors of overall survival when compared to cPDAC. Conclusion: PDAC is a heterogenous disease and accurate identification of variant histology is important for risk stratification, as these variants may have different biological behavior. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The use of nomograms in predicting the prognosis of early‐stage non‐small cell lung cancer (NSCLC), particularly in elderly patients, is not widespread. A validated prognostic model specifically for NSCLC patients over 80 years old holds promising potential for clinical application in forecasting patient outcomes.The prognostic value of various factors for NSCLC patients aged 80 and above was evaluated using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2017). Kaplan–Meier (KM) curves, Cox proportional hazards regression models, and nomogram were utilized to evaluate the impact of each factor on cancer‐specific survival (CSS).A cohort comprising 7045 individuals was selected for inclusion in the analysis. Through rigorous statistical analysis, 10 independent prognostic factors were identified and incorporated into the nomogram. The nomogram's receiver operating characteristic (ROC) curve area under the curve (AUC) was higher than that of the AJCC 7th edition TNM staging system's predicted CSS (0.744 versus 0.602), establishing the superior prognostic value of the nomogram.We have successfully created a highly accurate and discriminative nomogram that enables oncologists to predict the survival outcome of each individual patient with I/II NSCLC who is 80 years or older. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify a specific SCLC population that would benefit from surgery. Methods: This study utilized patient data retrieved from the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. To mitigate clinical biases, the propensity score matching (PSM) technique was employed. Separate cohorts were aligned using PSM according to the AJCC 8th edition TNM classification. The Kaplan–Meier method and a competing risk model were applied to evaluate overall survival (OS) and lung cancer–specific survival (LCSS), respectively. Outcomes: Among the 3394 patients with potentially resectable SCLC included in the study, 3062 underwent chemoradiotherapy and 332 underwent surgical treatment with adjuvant chemotherapy. Surgery was associated with better OS (median OS: 49 months; 95% CI: 35–63 months vs. 27 months; 95% CI: 21–33 months, p < 0.001) and LCSS (SHR, 0.578; 95% CI: 0.411–0.815, p < 0.001) in stage I patients after PSM. However, there was no significant difference in OS and LCSS between the surgery and nonsurgery groups in stage II and III patients after PSM. In the entire cohort, lobectomy was associated with improved OS (median OS: 48.6 vs. 28.7 months, p < 0.0001), but not LCSS (SHR, 0.696; 95% CI: 0.466–1.040, p = 0.078) compared with sublobar resection after PSM. Conclusion: Surgery with adjuvant chemotherapy significantly improved the survival prognosis of patients with early‐stage SCLC. However, surgical treatment should be carefully considered in patients with stage II/III disease. Lobectomy is oncologically equal to sublobar resection. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of sentinel lymph node biopsy (SLNB) versus axillary lymph node dissection (ALND) in managing early‐stage male breast cancer (MBC) patients with T1‐2 tumors and limited lymph node metastasis, all receiving radiotherapy, remains uncertain. This study examines trends and survival outcomes for SLNB and ALND in the United States. Methods: We conducted a retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) data from 2010 to 2020 for MBC patients with T1‐2 tumors and 1–2 positive lymph nodes undergoing radiotherapy. Patients were classified by nodes removed (SLNB ≤5, ALND ≥10), comparing overall survival (OS) and breast cancer‐specific survival (BCSS) between the groups before and after propensity score matching. Results: Of 299 MBC patients analyzed, SLNB usage increased from 18.8% in 2010 to 61.0% in 2020. Multivariable logistic regression highlighted significant associations of SLNB use with diagnosis year, race, surgery type, positive lymph node count, and metastasis size. No significant differences in 5‐year OS (77.98% SLNB vs. 85.85% ALND, p = 0.337) or BCSS (91.54% SLNB vs. 94.97% ALND, p = 0.214) were observed. Propensity score matching (96 patients per group) confirmed similar 5‐year OS (83.9% for SLNB vs. 82.0% for ALND, p = 0.925) and BCSS (90.1% for SLNB vs. 96.9% for ALND, p = 0.167). Conclusion: SLNB and ALND provide comparable survival outcomes in early‐stage MBC patients with limited lymph node metastasis undergoing radiotherapy. The increased utilization of SLNB supports its consideration to reduce surgical morbidity in selected MBC patients despite limited direct evidence. [ABSTRACT FROM AUTHOR]

Using a multi-institutional oncology database, we investigate the survival rates and the impacts of demographic, clinical, and management characteristics on overall survival among adult patients diagnosed with spinal ependymoma. Utilizing the SEER registry, patients with histologically or radiologically confirmed ependymomas were included. Factors impacting overall survival were analyzed using Kaplan-Meier survival curves and log-rank statistical analyses. A total of 1,580 patients were included. Their mean ± standard deviation age was 46.68 ± 15.96 years, and 51.1% were women. Gross total resection (GTR) was achieved in 66.4% of patients. The 5- and 10-year survival rates were 96.7% and 95.4%, respectively. A multivariable backward Cox regression showed that age ≥65 years was a significant predictor for mortality (hazard ratio [HR]: 3.93; 95% confidence interval [CI]: 2.21–7.00; P < 0.001). Likewise, tumor grade 3 (HR: 6.36; 95% CI: 1.95–20.76; P = 0.002), tumor grade 4 (HR: 7.74; 95% CI: 3.97–15.11; P < 0.001), presence of extra-neural metastasis (HR: 13.81; 95% CI: 3.67–51.96; P < 0.001), and receiving radiotherapy (HR: 2.50; 95% CI: 1.50–4.19; P < 0.001) were significant risk factors for mortality, while GTR was significantly associated with improved overall survival compared with subtotal resection or nonsurgical management (HR: 0.42; 95% CI: 0.25–0.73; P = 0.002). There were no significant effects for gender, race, marital status, income, residential area, chemotherapy, tumor size, and the presence of other benign or malignant tumors on the survival hazards (P > 0.05 for each). Early diagnosis and surgical management of spinal ependymomas, such as GTR, were associated with remarkable survival benefits. Old age, high-grade spinal ependymoma, and extra-neural metastasis were associated with worse overall survival, whereas radiotherapy's role remains unclear. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, researchers have begun to investigate the relation between sex and cancer. We wanted to look at the relation between sex and non-small cell lung cancer among high-risk patients to see if this relationship needs further exploration. In order to do this, we chose to look at large publicly available databases that contained information on lung cancer incidence. Our research found indications that females have an elevated risk of lung cancer incidence in comparison with males within high-risk populations. These results suggest that the difference in sex-specific cancer biomechanisms should be further investigated and explored. Background/Objectives: Non-small cell lung cancer (NSCLC) has seen a relative rise in incidence among females versus males in recent years, although males still have a higher overall incidence. However, it is unclear whether this trend is consistent across all populations. Therefore, we retrospectively examined this relationship in two large high-risk clinical cohorts. Methods: First, we analyzed lung cancer incidence among individuals with a smoking history of over 40 pack-years in the National Lung Screening Trial (NLST). Then, we investigated the incidence of second primary NSCLC in patients who underwent lobectomy for previous stage I lung cancer using the Surveillance, Epidemiology, and End Results (SEER) database. We performed both univariate and multivariable time-to-event analyses to investigate the relationship between sex and lung cancer incidence. Results: In the NLST cohort (n = 37,627), females had a higher risk of developing primary NSCLC than males (HR = 1.11 [1.007–1.222], p = 0.035) after adjusting for age and pack-year history. In the SEER cohort (n = 19,327), females again exhibited an increased risk of developing a second primary lung cancer (HR = 1.138 [1.02–1.269], p = 0.021), after adjusting for age, race, grade, and histology. Conclusions: Our analysis reveals that females have a modestly higher lung cancer incidence than males in high-risk populations. These findings underscore the importance of further researching the underlying cellular processes that may cause sex-specific differences in lung cancer incidence. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to explore the prognostic factors and survival patterns based on the histological type for the perimenopausal endometrial carcinoma (PIPEC) patients treated with hysterectomy. Methods: The PIPEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Methods of random survival forest (RSF) and Cox regression were used to identify the possible prognostic factors of PIPEC patients. Then overall survival (OS) and cancer-specific survival (CSS) of PIPEC data were analyzed by histological types with regional lymph nodes status and SEER-stage to investigate the survival patterns of the PIPEC patients. Results: A total of 14,178 PIPEC patients were included in the study. We found tumor size, grade, histology, SEER-stage, AJCC-stage, AJCC-T stage, metastasis to distant organs and regional lymph nodes status had a significant survival outcome for PIPEC both for OS and CSS (all p < 0.05). Regardless of regional lymph nodes status and SEER-stage for OS and CSS, the low-grade endometrioid carcinoma had the best prognosis outcome, followed by the mix cell adenocarcinoma and high-grade endometrioid carcinoma, while the carcinosarcoma and undifferentiated carcinoma had relatively poor prognosis outcome. And the survival patterns of different histological types of PIPEC were diverse and changed along with the time. Conclusion: We identified the possible prognostic factors of PIPEC patients treated with hysterectomy. And survival analysis based on the regional lymph nodes status and SEER-stage revealed the different histological types of PIPEC had diverse survival patterns, which will be helpful for guiding clinical practice. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to test for survival differences according to adjuvant chemotherapy (AC) status in radical nephroureterectomy (RNU) patients with pT2–T4 and/or N1–2 upper tract urothelial carcinoma (UTUC). Patients and Methods: Within the Surveillance, Epidemiology, and End Results database (SEER, 2007–2020), patients with UTUC treated with AC versus RNU alone were identified. Kaplan–Meier plots and multivariable Cox regression models addressed cancer-specific mortality (CSM). Results: Of 1995 patients with UTUC, 804 (40%) underwent AC versus 1191 (60%) RNU alone. AC rates increased from 36.1 to 57.0% over time in the overall cohort [estimated annual percentage changes (EAPC) ± 4.5%, p < 0.001]. The increase was from 28.8 to 50.0% in TanyN0 patients (EAPC ± 7.8%, p < 0.001) versus 50.0–70.9% in TanyN1–2 patients (EAPC ± 2.3%, p = 0.002). Within 698 patients harboring TanyN1–2 stage, median CSM was 31 months after AC versus 16 months in RNU alone (Δ = 15 months, p < 0.0001) and AC independently predicted lower CSM [hazard ratio (HR) 0.64; p < 0.001]. Similarly, within subgroup analyses according to stage, relative to RNU alone, AC independently predicted lower CSM in T2N1–2 (HR 0.49; p = 0.04), in T3N1–2 (HR 0.72; p = 0.015), and in T4N1–2 (HR 0.49, p < 0.001) patients. Conversely, in all TanyN0 as well as in all stage-specific subgroup analyses addressing N0 patients, AC did not affect CSM rates (all p > 0.05). Conclusions: In RNU patients, AC use is associated with significantly lower CSM in lymph-node-positive (N1–2) patients but not in lymph-node-negative patients (N0). The distinction between N1–2 and N0 regarding the effect of AC on CSM applied across all T stages from T2 to T4, inclusively. [ABSTRACT FROM AUTHOR]

Background: Non-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD. Methods: We retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms. Results: Several independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort. Conclusions: A dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

Abstract Purpose The aim of the study is to identify risk factors for the prognosis and survival of synchronous colorectal cancer and to create and validate a functional Nomogram for predicting cancer-specific survival in patients with synchronous colorectal cancer. Methods Synchronous colorectal cancers cases were retrieved from the Surveillance, Epidemiology, and End Results database retrospectively, then they were randomly divided into training (n = 3371) and internal validation (n = 1440) sets, and a set of 100 patients from our group was used as external validation. Risk factors for synchronous colorectal cancer were determined using univariate and multivariate Cox regression analyses, and two Nomograms were established to forecast the overall survival and cancer-specific survival, respectively. We assessed the Nomogram performance in terms of discrimination and calibration. Bootstrap resampling was used as an internal verification method, and we select external data from our hospital as independent validation sets. Results Two Nomograms are established to predict the overall survival and cancer-specific survival. In OS Nomogram, sex, age, marital status, ttumor pathological grade, AJCC TNM stage, preoperative serum CEA level, LODDS, radiotherapy and chemotherapy were determined as prognostic factors. In CSS Nomogram, age and marital status, AJCC TNM stage, tumor pathological grade, preoperative serum CEA level, LODDS, radiotherapy and chemotherapy were determined as prognostic factors.The C-indexes for the forecast of overall survival were 0.70, and the C-index was 0.68 for the training and internal validation cohort, respectively. The C-indexes for the forecast of cancer-specific survival were 0.75, and the C-index was 0.74 for the training and internal validation cohort, respectively. The Nomogram calibration curves showed no significant deviation from the reference line, indicating a good level of calibration. Both C-index and calibration curves indicated noticeable performance of newly established Nomograms. Conclusions Those Nomograms with risk rating system can identify high risk patients who require more aggressive therapeutic intervention and longer and more frequent follow-up scheme, demonstrated prognostic efficiency.

Abstract The main purpose of this study was to construct two nomograms to predict all-cause mortality (ACM) and cause-specific mortality (CSM) in non-muscle-invasive bladder cancer (NMIBC) patients after transurethral resection of bladder tumors (TURBTs). We selected NMIBC patients who underwent TURBT between 2004 and 2017 from the Surveillance, Epidemiology, and End Results database. The patients were randomly divided into a training set and a validation set at a ratio of 7:3. The independent influencing factors of ACM and CSM in the training set were determined by univariate and multivariate Cox regression analyses. We then integrated those independent influencing factors to construct nomograms. These prediction nomograms were further verified in the validation set. The C-index, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve were used to evaluate the identification, calibration, predictive ability and clinical effectiveness of the nomograms. A total of 28,086 cases were ultimately included in this study, which were divided into a training set (19,661 individuals) and a validation set (8425 individuals). Nine variables, including age at diagnosis, race, marital status, tumor grade, T stage, tumor size, number of tumors, and primary site, were obtained via multivariate Cox regression of the training set and used to construct two nomograms prediction model. The C-index values for the ACM nomogram were 0.743 and 0.741 for the training and validation sets, respectively. Moreover, the corresponding values of the C-index for the CSM nomogram were 0.785 and 0.786, respectively. The ROC curves, calibration curves, and DCA curves showed good predictive performance. The nomograms can assist clinicians in identifying high-risk populations and devising more individualized treatment strategies for NMIBC patients.

Abstract Background This study aimed to predict metastasis risk and identify prognostic factors of gastrointestinal signet ring cell carcinoma (SRCC) using data from the SEER database, the largest cancer dataset in North America. Methods Data were obtained from the SEER database, covering 17 cancer registries from 2004 to 2020. Demographic and clinical data included sex, age, race, tumor location, size, pathological grade, stage, overall survival time, and treatment modalities. Statistical analyses were conducted using SPSS and R software. Propensity Score Matching (PSM) ensured comparable baseline characteristics between gastric cancer (GC) and colorectal cancer (CRC) groups. LASSO regression analysis identified predictors of metastasis, leading to the construction of predictive models using the lrm function in R. Nomograms were visualized with the “rms” package and assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Cox regression analyses identified prognostic indicators for overall survival (OS), and Kaplan–Meier curves compared OS between high-risk and low-risk groups. Results From 2004 to 2020, 7680 SRCC patients were identified, including 4980 GC and 2700 CRC patients. CRC patients were older and had larger tumors, higher staging, and worse differentiation. Nomograms demonstrated good discriminative ability, with AUCs of 0.704 and 0.694 for GC, and 0.694 and 0.701 for CRC in training and validation cohorts, respectively. The DCA curve indicates that this predictive model has a high gain in predicting metastasis and OS. Conclusions The nomograms effectively predicted metastasis risk and OS in metastatic SRCC patients, offering clinical utility in stratifying patients and guiding treatment decisions, thereby enhancing personalized treatment approaches.

Abstract There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (CN) or radical nephrectomy that includes simultaneous resection of the parenchyma, affected perirenal fascia, perirenal fat, and ureter (RN) relative to partial nephrectomy (PN) for patients with nonmetastatic (M0) renal cell carcinoma (RCC) in terms of overall survival (OS). This study aimed to evaluate the effect of different nephrectomy on the OS of M0 RCC and to identify the main beneficiaries of different nephrectomy. The data was collected from the Surveillance, Epidemiology and End Results (SEER) database. Kaplan–Meier plots, and multivariable Cox regression models were used. Propensity score matching (PSM) was performed to reduce the effect of selection bias. A prognostic model for M0 RCC patients after nephrectomy was established using the deep learning framework. Stage I M0 RCC patients who received PN did not have a better prognosis than none surgery in terms of CSS and OS after PSM. Stage I M0 RCC patients who received PN did not have a better prognosis than CN in terms of CSS but had in terms of OS after PSM. Stage I M0 RCC patients who received PN had a better prognosis in terms of CSS and OS after PSM. The test set AUC values for 1/3/5/7/9 year survival prediction of the surgical decision model were 0.844, 0.812, 0.794, 0.79, 0.813. For OS and CSS in M0 RCC patients, PN was superior to CN and RN regardless of grade. But for grade I, PN and none surgery did not show significant differences. In order to further categorize surgical patients and make more precise decisions regarding their treatment, a surgical decision-making model has been established.

Abstract Purpose This study aimed to identify the risk factors associated with ampullary duodenal carcinoma (a-DC) and develop a clinical model to dynamically and accurately predict the risk of lymph node metastasis (LNM) in a-DC patients. Methods Data from 4077 patients (2004–2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010–2020) from Zhejiang Provincial People’s Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram’s accuracy and generalizability were externally validated.. Results After screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P

Abstract Purposes Whether patients with early-onset colorectal cancer (EOCRC) receive chemotherapy has been controversial, so our study aimed to screen patients with EOCRC who benefit from chemotherapy. Methods A total of 2166 EOCRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into chemotherapy and non-chemotherapy groups, propensity score matching (PSM) was performed to balance the differences between the groups, and the Kaplan–Meier method was used to calculate the cancer-specific survival (CSS) of EOCRC patients. Multifactorial COX regression analysis was used to identify independent prognostic factors for CSS and to construct a nomogram for predicting CSS in EOCRC patients. The overall risk score was calculated based on Nomogram, and EOCRC patients were classified into high-risk and low-risk groups to assess further chemotherapy's therapeutic effect on patients with different risk stratification. Results Before PSM, patients in the chemotherapy group had poorer CSS (p

Abstract Background Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk. Methods Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer. Results Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model’s area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach. Conclusion We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans.

Lin-Feng Guo,&ast; Ya-Qing Dai,&ast; Yi-Feng Yu, San-Gang Wu Department of Radiation Oncology, Xiamen Cancer Quality Control Center, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yi-Feng Yu; San-Gang Wu, Department of Radiation Oncology, Xiamen Cancer Quality Control Center, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, People’s Republic of China, Email yuyfeng001@sina.com; wusg@xmu.edu.cnPurpose: To investigate the prognostic implications of gender in nasopharyngeal carcinoma (NPC) utilizing data from two independent cohorts: the Xiamen (XM)-NPC cohort (an endemic area in China) and the United States Surveillance, Epidemiology, and End Results (SEER)-NPC cohort (a non-endemic area).Methods: We included patients diagnosed with NPC from both the XM-NPC and SEER-NPC cohorts. Statistical analysis involved the chi-square test, Kaplan-Meier method, and multivariate Cox regression analyses.Results: The study identified 728 patients in the XM-NPC cohort and 2237 in the SEER cohort. In the XM-NPC cohort, 515 (70.7%) were male and 213 (29.3%) were female. In the SEER-NPC cohort, 1597 (71.4%) were male and 640 (28.6%) were female. The male-to-female ratio peaked at ≤ 25 years (2.33) and 46– 55 years (2.79) in the XM-NPC cohort, and at ≤ 25 years (2.07) and 56– 65 years (3.24) in the SEER-NPC cohort. The lowest ratios were observed among patients aged 26– 35 years in both cohorts (XM-NPC: 1.64; SEER-NPC:1.38). In the XM-NPC cohort, females had significantly better overall survival (P=0.022) and distant metastasis-free survival (P=0.038) compared to males. Similarly, in the SEER-NPC cohort, gender was found to be an independent prognostic factor for overall survival, with females showing better outcomes (P=0.038). Consistent trends were observed in patients aged > 45 years in both cohorts, while survival outcomes were comparable between genders in patients aged ≤ 45 years.Conclusion: Gender independently influences survival outcomes of NPC in both endemic and non-endemic areas.Keywords: nasopharyngeal carcinoma, gender, SEER, Chinese, outcome

Abstract Background The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients. Methods Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform. Results We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform ( https://wubopredict.shinyapps.io/dynnomapp/ ). Conclusion The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Abstract Pilocytic astrocytoma (PA) is classified as a Grade I benign neuroglial tumor. The extent of surgical resection is a critical factor influencing the prognosis for patients with PA. In prior researches of PA, the extent of surgical resection is generally categorized into GTR, STR and biopsy. In some researches on brain tumor surgeries, the extent of resection also includes GTL. There is no existing research specifically comparing the efficacy of GTR versus GTL in PA treatment. In this study, the data we used are from the SEER database. We categorized the extent of resection into GTL, GTR, STL, STR, biopsy, and no surgery based on SEER classification of surgical procedures, to investigate the impact of extent of resection on PA patient survival. A multivariate logistic regression model was utilized to acquire odds ratios (OR) for different extent of resection. Survival outcomes across different extent of resection (GTL, GTR, STL, STR, biopsy, no surgery) were assessed using Kaplan–Meier survival curve analysis, with curve comparisons conducted via log-rank tests. The impact of various risk factors on survival was assessed using the Cox proportional hazards model. The hazard ratio (HR) was employed to quantify the influence of one or more factors on overall survival throughout the follow-up period. Multivariate Cox analysis revealed that age, tumor location, extent of resection, as well as the application of radiotherapy and chemotherapy, all significantly impacted prognosis. Compared to GTL, GTR did not significantly increase the risk of mortality (HR 1.17; 95% CI 0.73–1.86, p = 0.5). Furthermore, there was no statistically significant difference between the Kaplan–Meier survival curves of the two groups (p = 0.18). We employed propensity score matching (PSM) to balance the differences in baseline characteristics of patients receiving chemotherapy or radiotherapy. A total of 4429 patients were included in this study. Age, diagnosis period, race, tumor size, and tumor location as influential on the extent of resection. Age, tumor location, extent of resection, and application of radiotherapy and chemotherapy influenced the survival of PA patients. The Kaplan–Meier survival curves revealed that the long-term survival rate for GTR is slightly higher than that for GTL. The PSM analysis revealed that the application of radiotherapy and chemotherapy was associated with the reduction of overall survival in PA patients. In conclusion, there was no significant difference in survival between GTR and GTL, so GTR with less damage was preferred. The application of radiotherapy and chemotherapy can reduce overall survival of patients with PA.

Abstract Background There have been several reports showing that heart-related deaths are common in long-term survivors of esophageal cancer after radiation therapy; however, radiotherapy technology is evolving year by year. This study was carried out using the SEER database to determine whether the frequency of mortality from heart disease after radiotherapy has improved over time in patients with esophageal cancer. Methods SEER*Stat statistical software version 8.3.9.2 (National Cancer Institute) was used to perform case listing and data extraction. We reviewed causes of death in 8,297 patients who were treated by radiotherapy without surgery between 2004 and 2015 (radiotherapy group). For comparison with this group, we also reviewed causes of death in 5,149 patients who were treated by surgery without radiotherapy (surgery group). Results In the radiotherapy group, the cumulative heart-related death rate in patients with carcinoma in the middle to abdominal esophagus, for which it was considered that the heart was irradiated with a higher dose, was significantly higher than that in patients with carcinoma in the cervical to upper thoracic esophagus (p = 0.017). However, in the surgery group, the cumulative heart-related death rate in patients with carcinoma in the middle to abdominal esophagus tended to be lower than that in patients with carcinoma in the cervical to upper thoracic esophagus (p = 0.063). The cumulative heart-related death rate in patients treated in 2010–2015 was significantly lower than that in patients treated in 2004–2009 in the radiotherapy group (p = 0.011), although the cumulative heart-related death rate was not significantly different between patients treated in 2010–2015 and patients treated in 2004–2009 in the surgery group (p = 0.90). Conclusions The results suggest that recent advances in radiotherapy have enabled a reduction in radiation-induced heart disease in patients with esophageal cancer.

Abstract Purpose This study aimed to explore the prognostic factors and survival patterns based on the histological type for the perimenopausal endometrial carcinoma (PIPEC) patients treated with hysterectomy. Methods The PIPEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Methods of random survival forest (RSF) and Cox regression were used to identify the possible prognostic factors of PIPEC patients. Then overall survival (OS) and cancer-specific survival (CSS) of PIPEC data were analyzed by histological types with regional lymph nodes status and SEER-stage to investigate the survival patterns of the PIPEC patients. Results A total of 14,178 PIPEC patients were included in the study. We found tumor size, grade, histology, SEER-stage, AJCC-stage, AJCC-T stage, metastasis to distant organs and regional lymph nodes status had a significant survival outcome for PIPEC both for OS and CSS (all p