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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]

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]

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 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 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 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 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 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 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 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 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 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 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

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]

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]

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 FROM AUTHOR]

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

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 FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

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 < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery. [ABSTRACT FROM AUTHOR]

Abstract Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.

Abstract With the rapid development of imaging technology and comprehensive treatment in modern medicine, the early diagnosis rate of breast cancer is constantly improving, and the prognosis is also improving; As breast cancer patients survive longer, the risk of developing second primary cancers increases. Since both breast and thyroid are Hormone receptor sensitive organs, which are regulated by hypothalamus pituitary target gland endocrine axis, changes in body endocrine status may lead to the occurrence of these two diseases in succession or simultaneously. This study extracted clinical data and survival outcomes of breast cancer patients registered in the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2019. After matching the case and controls with propensity scores, the selected patients were randomly split into training and test datasets at a ratio of 7:3. Univariate and multivariate COX proportional regression analysis is used to determine independent risk factors for secondary thyroid cancer and construct a column chart prediction model. Age, ethnicity, whether radiotherapy, tumor primary location, N stage, M stage were identified by Cox regression as independent factors affecting secondary thyroid cancer in patients with breast cancer patients, and a risk factor nomogram was established to predict patients’ 3 and 5 year survival probabilities. The AUC values for 3 and 5 years in the training set were 0.713, 0.707, and the c-index was 0.693 (95% CI 0.67144, 0.71456), and the AUC values for 3 and 5 years in the validation set were 0.681, 0.681, and the c-index was 0.673 (95% CI 0.64164, 0.70436), respectively.

Abstract Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Abstract Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

Abstract Medullary Carcinoma of the Colon (MCC) is a rare histological subtype of colon cancer, and there is currently no recognized optimal treatment plan for it, with its prognosis remaining unclear. The aim of this study is to analyze the independent prognostic factors for MCC patients and develop and validate nomograms to predict overall survival (OS). A total of 760 patients newly diagnosed with MCC from 2004 to 2020 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to a training group and a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The nomogram prediction model was evaluated and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study found that elderly women are more susceptible to MCC, and the ascending colon and cecum are the most common sites of involvement. MCC is poorly differentiated, with stages II and III being the most common. Surgery is the primary treatment for MCC. The prognosis for patients with stage IV MCC is poor, with a median survival time of only 10 months. Independent prognostic factors for MCC include age, N stage, M stage, surgery, chemotherapy, and tumor size. Among them, age 7 cm (HR = 1.66, 95%CI 1.20–2.30) were risk factors for colon medullary carcinoma. The results of ROC, AUC, calibration curves, and DCA demonstrate that the nomogram prediction model exhibits good predictive performance. We have updated the demographic characteristics of colon medullary carcinoma and identified age, N staging, M staging, surgery, chemotherapy and tumor size as independent prognostic factors for colon medullary carcinoma. Additionally, we have established nomograms for prognostic prediction. These nomograms can provide personalized predictions and serve as valuable references for clinical decision-making.

Abstract Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

Abstract Cancers of the kidney and renal pelvis are among the most prevalent types of urinary cancers. We aimed to outline the incidence trends of kidney and renal pelvis cancers by age, sex, race/ethnicity, and histology in the United States (US) from 2000 to 2020. The data was obtained from the Surveillance, Epidemiology, and End Results (SEER) 22 database. The identification of patients with kidney and renal pelvis cancers with morphologies of renal cell carcinoma, nephroblastoma, sarcoma, and neuroendocrine tumor was conducted utilizing the International Classification of Diseases for Oncology version 3. The average annual percent change (AAPC) were presented. All estimates were given in the form of counts and delayed age-standardized incidence rates (ASIRs) per 100,000 people. From 2000 to 2019, a total of 490,481 cases of kidney and renal pelvic cancer were recorded across all age groups in the US. The majority of them were among Non-Hispanic Whites (NHWs) (69.75%) and those aged 55–69 years (39.96%). The ASIRs per 100,000 for kidney and pelvis cancers were 22.03 for men and 11.14 for women. Non-Hispanic Black men had the highest ASIR (24.53 [24.24, 24.81]), and increase in ASIR over the 2000–2019 period (AAPC: 2.19% [1.84, 2.84]). There was a noticeable increase in incidence of kidney and renal pelvis cancers. Individuals aged 70–84 years had the highest ASIR for kidney and renal pelvis cancers. The COVID-19 era has resulted in a significant reduction in incidence rates across all demographics.

Abstract Cancer-directed surgeries (CDS) play a crucial role in prostate cancer (PCa) management along with possible survival and therapeutic benefits. However, barriers such as socioeconomic factors may affect patients’ decision of refusing recommended CDS. This study aimed to uncover risk factors and the impact on survival associated with CDS refusal. We retrospectively reviewed the Surveillance, Epidemiology, and End Results database for patients diagnosed with PCa between 2000 and 2019. Multiple sociodemographic and clinical characteristics were extracted to assess predictors for physicians’ surgical recommendations and patients’ surgical refusal, respectively. Propensity score matching was performed to balance the covariates. The impact of surgical refusal on mortality risk was also investigated. A total of 185,540 patients were included. The physician’s recommendation of CDS was significantly influenced by the patient’s age, race, income, home location, diagnosis year, Gleason score, prostate-specific antigen (PSA), and TNM stage. About 5.6% PCa patients refused CDS, most of whom were older, non-White race, lack of partners, living outside of metropolitan areas, with higher PSA or lower clinical TNM stage. Patients who refused CDS had an increased risk of cancer-specific mortality and overall mortality than those who performed CDS. Physicians may weigh a host of sociodemographic and clinical factors prior to making a CDS recommendation. Patients’ refusal of recommended CDS affected survival and was potentially modifiable by certain sociodemographic factors. Physicians should fully consider the hindrances behind patients’ CDS refusal to improve patient-doctor shared decision-making, guide patients toward the best alternative and achieve better outcomes.

Abstract The survival significance of the number of positive lymph nodes in salivary gland carcinoma remains unclear. Thus, the current study aimed to determine the effect of the number of positive lymph nodes on disease-specific survival (DSS) and overall survival (OS) in cN0 mucoepidermoid carcinoma (MEC) of the major salivary gland. Patients surgically treated for MEC of the major salivary gland between 1975 and 2019 were retrospectively enrolled from the surveillance, epidemiology, and end results database. The total population was randomly divided into training and test groups (1:1). Primary outcome variables were DSS and OS. Prognostic models were constructed based on the independent prognostic factors determined using univariate and multivariate Cox analyses in the training group and were validated in the test group using C-index. A total of 3317 patients (1624 men and 1693 women) with a mean age of 55 ± 20 years were included. The number of positive lymph nodes was an independent prognostic factor for both DSS and OS, but the effect began when at least two positive lymph nodes for DSS and three positive lymph nodes for OS were found. Predictive models for DSS and OS in the training group had C-indexes of 0.873 (95% confidence interval [CI] 0.853–0.893) and 0.835 (95% CI 0.817–0.853), respectively. The validation of the test group showed C-indexes of 0.877 (95% CI 0.851–0.902) for DSS and 0.820 (95% CI 0.798–0.842) for OS. The number of positive lymph nodes was statistically associated with survival in cN0 major salivary gland MEC. The current prognostic model could provide individualized follow-up strategies for patients with high reliability.

Abstract Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

Abstract To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.

Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool. [ABSTRACT FROM AUTHOR]

Medullary Carcinoma of the Colon (MCC) is a rare histological subtype of colon cancer, and there is currently no recognized optimal treatment plan for it, with its prognosis remaining unclear. The aim of this study is to analyze the independent prognostic factors for MCC patients and develop and validate nomograms to predict overall survival (OS). A total of 760 patients newly diagnosed with MCC from 2004 to 2020 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to a training group and a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The nomogram prediction model was evaluated and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study found that elderly women are more susceptible to MCC, and the ascending colon and cecum are the most common sites of involvement. MCC is poorly differentiated, with stages II and III being the most common. Surgery is the primary treatment for MCC. The prognosis for patients with stage IV MCC is poor, with a median survival time of only 10 months. Independent prognostic factors for MCC include age, N stage, M stage, surgery, chemotherapy, and tumor size. Among them, age < 75 years and completion of chemotherapy were protective factors for colon medullary carcinoma, while N2 (HR = 2.18, 95%CI 1.40–3.38), M1 (HR = 3.31, 95%CI 2.01–5.46), no surgery (HR = 27.94, 95%CI 3.69–211.75), and tumor diameter > 7 cm (HR = 1.66, 95%CI 1.20–2.30) were risk factors for colon medullary carcinoma. The results of ROC, AUC, calibration curves, and DCA demonstrate that the nomogram prediction model exhibits good predictive performance. We have updated the demographic characteristics of colon medullary carcinoma and identified age, N staging, M staging, surgery, chemotherapy and tumor size as independent prognostic factors for colon medullary carcinoma. Additionally, we have established nomograms for prognostic prediction. These nomograms can provide personalized predictions and serve as valuable references for clinical decision-making. [ABSTRACT FROM AUTHOR]

Cancers of the kidney and renal pelvis are among the most prevalent types of urinary cancers. We aimed to outline the incidence trends of kidney and renal pelvis cancers by age, sex, race/ethnicity, and histology in the United States (US) from 2000 to 2020. The data was obtained from the Surveillance, Epidemiology, and End Results (SEER) 22 database. The identification of patients with kidney and renal pelvis cancers with morphologies of renal cell carcinoma, nephroblastoma, sarcoma, and neuroendocrine tumor was conducted utilizing the International Classification of Diseases for Oncology version 3. The average annual percent change (AAPC) were presented. All estimates were given in the form of counts and delayed age-standardized incidence rates (ASIRs) per 100,000 people. From 2000 to 2019, a total of 490,481 cases of kidney and renal pelvic cancer were recorded across all age groups in the US. The majority of them were among Non-Hispanic Whites (NHWs) (69.75%) and those aged 55–69 years (39.96%). The ASIRs per 100,000 for kidney and pelvis cancers were 22.03 for men and 11.14 for women. Non-Hispanic Black men had the highest ASIR (24.53 [24.24, 24.81]), and increase in ASIR over the 2000–2019 period (AAPC: 2.19% [1.84, 2.84]). There was a noticeable increase in incidence of kidney and renal pelvis cancers. Individuals aged 70–84 years had the highest ASIR for kidney and renal pelvis cancers. The COVID-19 era has resulted in a significant reduction in incidence rates across all demographics. [ABSTRACT FROM AUTHOR]

Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model's clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance. [ABSTRACT FROM AUTHOR]

To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients. [ABSTRACT FROM AUTHOR]

Abstract Due to the long-term low survival rates of gastric adenocarcinoma (GAC) patients, the occurrence and prognosis of second primary malignancies (SPMs) are often underreported and overlooked as a significant concern.To date, only a few studies have addressed this issue in the context of GAC. These studies, however, are limited by their small patient cohorts and lack of substantial, meaningful findings. Our study aims to fill this gap by investigating the incidence, risk factors, and prognostic significance of SPMs among GAC survivors. Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we analysed data from patients diagnosed with GAC between 2000 and 2020. The study employs the standardized incidence ratio (SIR) to assess the relative risk of SPMs, competing risk regression to identify risk factors for SPM development after GAC, and Kaplan-Meier and COX regression analyses for survival outcomes. Out of 44,041 GAC patients analyzed, 2,032 (4.3%) developed SPMs, with a median latency period of 36 months. The incidence of SPMs was significantly higher in GAC patients (SIR 1.36, 95% CI 1.32-1.4, EAR 53.57) compared to the general population. Key factors including older age, sex, tumor grade, summary stage, and history of surgical and radiation therapy were related to the higher risk of developing SPMs following GAC. Interestingly, GAC patients without SPMs exhibited poorer overall survival compared to those with SPMs. Age, summary stage, and surgical history were identified as independent prognostic factors for GAC patients with SPMs. This comprehensive analysis underscores the necessity of vigilant monitoring and tailored follow-up for SPMs in GAC survivors, highlighting the study's contribution to enhancing GAC survivors care strategies.

Abstract Chronic Eosinophilic Leukemia (CEL), a rare and intricate hematological disorder characterized by uncontrolled eosinophilic proliferation, presents clinical challenges owing to its infrequency. This study aimed to investigate epidemiology and develop a prognostic nomogram for CEL patients. Utilizing the Surveillance, Epidemiology and End Results database, CEL cases diagnosed between 2001 and 2020 were analyzed for incidence rates, clinical profiles, and survival outcomes. Patients were randomly divided into training and validation cohorts (7:3 ratio). LASSO regression analysis and Cox regression analysis were performed to screen the prognostic factors for overall survival. A nomogram was then constructed and validated to predict the 3- and 5-year overall survival probability of CEL patients by incorporating these factors. The incidence rate of CEL was very low, with an average of 0.033 per 100,000 person-years from 2001 to 2020. The incidence rate significantly increased with age and was higher in males than females. The mean age at diagnosis was 57 years. Prognostic analysis identified advanced age, specific marital statuses, and secondary CEL as independent and adverse predictors of overall survival. To facilitate personalized prognostication, a nomogram was developed incorporating these factors, demonstrating good calibration and discrimination. Risk stratification using the nomogram effectively differentiated patients into low- and high-risk groups. This study enhances our understanding of CEL, offering novel insights into its epidemiology, demographics, and prognostic determinants, while providing a possible prognostication tool for clinical use. However, further research is warranted to elucidate molecular mechanisms and optimize therapeutic strategies for CEL.

Due to the long-term low survival rates of gastric adenocarcinoma (GAC) patients, the occurrence and prognosis of second primary malignancies (SPMs) are often underreported and overlooked as a significant concern.To date, only a few studies have addressed this issue in the context of GAC. These studies, however, are limited by their small patient cohorts and lack of substantial, meaningful findings. Our study aims to fill this gap by investigating the incidence, risk factors, and prognostic significance of SPMs among GAC survivors. Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we analysed data from patients diagnosed with GAC between 2000 and 2020. The study employs the standardized incidence ratio (SIR) to assess the relative risk of SPMs, competing risk regression to identify risk factors for SPM development after GAC, and Kaplan-Meier and COX regression analyses for survival outcomes. Out of 44,041 GAC patients analyzed, 2,032 (4.3%) developed SPMs, with a median latency period of 36 months. The incidence of SPMs was significantly higher in GAC patients (SIR 1.36, 95% CI 1.32-1.4, EAR 53.57) compared to the general population. Key factors including older age, sex, tumor grade, summary stage, and history of surgical and radiation therapy were related to the higher risk of developing SPMs following GAC. Interestingly, GAC patients without SPMs exhibited poorer overall survival compared to those with SPMs. Age, summary stage, and surgical history were identified as independent prognostic factors for GAC patients with SPMs. This comprehensive analysis underscores the necessity of vigilant monitoring and tailored follow-up for SPMs in GAC survivors, highlighting the study's contribution to enhancing GAC survivors care strategies., (© 2024. The Author(s).)