Surveillance, Epidemiology, And End Results Population-based Study Research Articles

Evaluation of the association between lymph node ratio and long-term survival in patients after surgery for lymph node-positive bladder cancer: a SEER population-based study with external validation

BackgroundThe lymph node ratio (LNR) has been recognized as an emerging prognostic biomarker in various malignant tumors. Our study aimed to investigate the prognostic role of LNR in postoperative patients with lymph node-positive bladder cancer.MethodsThis study comprised a total of 3911 eligible patients diagnosed with lymph node-positive bladder cancer. This included 3767 patients from the Surveillance, Epidemiology, and End Results (SEER) database and 144 patients from two Chinese hospitals forming the external validation cohort. We used X-tile software to identify the optimal cut-off value for LNR. The Kaplan-Meier method and Cox regression model were utilized to evaluate the association between LNR and overall survival (OS) and cancer-specific survival (CSS). Based on the LNR index, two nomograms were constructed to estimate the prognosis of patients with lymph node-positive bladder cancer. The discriminant ability and accuracy of the nomogram were tested using the receiver operating characteristic (ROC) curve, calibration curves and decision curve analysis.ResultsThe Kaplan-Meier survival curves, stratified by LNR, demonstrated significant differences in overall and cancer-specific survival rates (P < 0.05). After adjusting for clinical and tumor factors, including AJCC N staging, patients with an LNR greater than 0.3 exhibited significantly worse OS and CSS compared to those with an LNR less than 0.1 in both the SEER and external validation cohorts. Furthermore, the nomogram, which incorporated LNR, showed satisfactory discriminative ability, and the calibration curves confirmed favorable consistency.ConclusionLNR proves to be an independent prognostic factor for postoperative patients with lymph node-positive bladder cancer. These findings highlight LNR’s potential as a prognostic indicator, which could be beneficial in patient consultations and guiding treatment decisions.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Prognostic factors of cutaneous soft tissue sarcomas in children: a SEER population-based study.

This study aims to analyze the clinicopathological characteristics and survival outcomes of cutaneous soft tissue sarcomas (CSTS) in children. We selected pediatric cases of CSTS diagnosed between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. Survival rates were calculated using Kaplan-Meier methods. We performed univariate analyses with the log-rank test and multivariate survival analyses using Cox proportional-hazards models to determine factors affecting overall survival (OS). Additionally, we constructed a predictive nomogram based on the outcomes of the Cox regression. A total of 148 pediatric patients with CSTS were reviewed. The median age at diagnosis was 13 years (range: 0-18 years). Prognostically, tumors located on the extremities showed better outcomes compared to those on the head, neck, or trunk. Among the histological types, angiosarcoma had the lowest five-year survival rate at 51.3%, which was substantially lower compared to fibrous histiocytoma and leiomyosarcoma. Cox regression analysis highlighted surgical intervention as the only significant independent prognostic factor for OS, with an increased risk of mortality observed in patients not undergoing surgery. Additionally, patients with distant-stage disease exhibited significantly lower survival rates than those with localized conditions. Pediatric CSTS represents a diverse and infrequent group of tumors, predominantly fibrous histiocytoma and leiomyosarcoma. Surgery was identified as the crucial determinant of survival, underscoring its role in effectively managing these patients.

Effect of elevated CEA levels on the outcome of colorectal cancer patients with different histopathologic types: A SEER population-based study.

Limited and contradictory evidence has been reported regarding the prognostic effects of carcinoembryonic antigen (CEA) on the prognosis and metastasis of classical adenocarcinoma (CA), mucinous adenocarcinoma (MA), and signet-ring cell carcinoma (SRCC) in colorectal cancer patients. We investigated the associations between histological subtypes and preoperative serum CEA levels in determining the oncologic outcomes of colorectal cancer (CRC) patients. A total of 47,692 patients with clearly diagnosed CRC were selected from the Surveillance, Epidemiology, and End Results (SEER) database and divided into two cohorts based on serum CEA levels: CEA-normal (C0) and CEA-elevated (C1). Chi-square analysis revealed a correlation between CEA levels and histological classification. We then included a newly defined interaction variable (H&CEA) in the Cox regression analysis, which demonstrated that this variable could serve as an independent prognostic factor (P<0.001). CA, in the context of elevated serum CEA levels, differed from the other two histopathological types, showing unexpectedly higher risks for both OS (HR = 1.70, 95% CI = 1.65-1.75, P<0.001) and CSS (HR = 1.78, 95% CI = 1.72-1.85, P<0.001). Furthermore, elevated CEA levels significantly increased the proportion of liver metastases in the CA group (25.43% vs. 3.95%, P<0.001). The interaction variable H&CEA can be used as an independent prognostic factor for CRC and should be considered in the diagnosis of CRC and the development of personalized treatment plans. Additionally, in the context of elevated CEA levels, CA is associated with poor prognosis and increased liver metastases. This CRC subgroup warrants special clinical attention from oncologists.

Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study

BackgroundInvasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.ObjectiveThis study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma.MethodsA total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients.ResultsCox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863.ConclusionsA machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.

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

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

A nomogram for overall survival of second primary cancers following upper-tract urothelial carcinoma: a SEER population-based study.

With improving prognosis in upper-tract urothelial carcinoma (UTUC), an increasing number of second primary malignancies (SPMs) are being identified. However, there is limited research on SPMs following UTUC. This study aims to evaluate the risk of SPMs in UTUC patients and create a nomogram to predict their survival rates. Utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database, we assessed the risk of SPMs among UTUC patients. Additionally, we developed and validated an overall survival (OS) nomogram for SPM patients post-UTUC diagnosis. The prevalence of SPMs among UTUC patients was 30.23%, with solid tumors being the most prevalent type of second malignancy, constituting 95.30% of all SPMs. The overall risk of SPMs was significantly elevated across all subgroups. Univariate and multivariate Cox regression analyses identified age, race, gender, UTUC SEER historic stage, surgery, SPM site, histologic type, grade, and SEER historic stage as independent prognostic factors for SPM OS. Subsequently, we developed a nomogram for predicting SPM OS. The C-index for the training and validation sets were 0.72 [95% confidence interval (CI): 0.70-0.74] and 0.71 (95% CI: 0.67-0.75), respectively. The area under the curve (AUC) demonstrated good performance of our model in predicting the 3-year (0.73 and 0.737) and 5-year (0.723 and 0.733) OS of SPMs in both sets. This study represents the first comprehensive analysis of SPM incidence in UTUC patients and introduces a nomogram for predicting SPM prognosis.

Association of Definitive Radiotherapy for Esophageal Cancer and the Incidence of Secondary Head and Neck Cancers: A SEER Population-Based Study.

Impact of radiotherapy (RT) for esophageal cancer (EC) patients on the development of secondary head and neck cancer (SHNC) remains equivocal. The objective of this study was to investigate the link between definitive RT used for EC treatment and subsequent SHNC. This study was conducted using the Surveillance, Epidemiology, and End Results (SEER) database to collect the data of primary EC patients. Fine-Gray competing risk regression and standardized incidence ratio (SIR) and propensity score matching (PSM) method were used to match SHNC patients with only primary head and neck cancer (HNC) patients. Overall survival (OS) rates were applied by Kaplan-Meier analysis. In total, 14,158 EC patients from the SEER database were included, of which 9,239 patients (65.3%) received RT and 4,919 patients (34.7%) received no radiation therapy (NRT). After a 12-month latency period, 110 patients (1.2%) in the RT group and 36 patients (0.7%) in the NRT group experienced the development of SHNC. In individuals with primary EC, there was an increased incidence of SHNC compared to the general US population (SIR = 5.95, 95% confidence interval (CI): 5.15 - 6.84). Specifically, the SIR for SHNC was 8.04 (95% CI: 6.78 - 9.47) in the RT group and 3.51 (95% CI: 2.64 - 4.58) in the NRT group. Patients who developed SHNC after RT exhibited significantly lower OS compared to those after NRT. Following PSM, the OS of patients who developed SHNC after RT remained significantly lower than that of matched patients with only primary HNC. An association was discovered between RT for EC and increased long-term risk of SHNC. This work enables radiation oncologists to implement mitigation strategies to reduce the long-term risk of SHNC in patients who have received RT following primary EC.

Nomograms predict survival in elderly women with triple-negative breast cancer: A SEER population-based study.

The effective treatment of breast cancer in elderly patients remains a major challenge. To construct a nomogram affecting the overall survival of triple-negative breast cancer (TNBC) and establish a survival risk prediction model. A total of 5317 TPBC patients with negative expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) who were diagnosed and received systematic treatment from 2010 to 2015 were collected from the American Cancer Surveillance, Epidemiology and End Results (SEER) database. They were randomly divided into training set (n= 3721) and validation set (n= 1596). Univariate and multivariate Cox regression analysis were used to identify prognostic features, and a nomogram was established to predict the probability of 1-year, 3-year and 5-year OS and BCSS. We used consistency index (C-index), calibration curve, area under the curve (AUC) and decision curve analysis (DCA) to evaluate the predictive performance and clinical utility of the nomogram. The C-indices of the nomograms for OS and BCSS in the training cohort were 0.797 and 0.825, respectively, whereas those in the validation cohort were 0.795 and 0.818, respectively. The receiver operating characteristic (ROC) curves had higher sensitivity at all specificity values as compared with the Tumor Node Metastasis (TNM) system. The calibration plot revealed a satisfactory relationship between survival rates and predicted outcomes in both the training and validation cohorts. DCA demonstrated that the nomogram had clinical utility when compared with the TNM staging system. This study provides information on population-based clinical characteristics and prognostic factors for patients with triple-negative breast cancer, and constructs a reliable and accurate prognostic nomogram.

Prognostic factors of tongue cancer in children and adolescents: A SEER population-based study.

This study aims to analyze the clinicopathological characteristics and survival outcomes of tongue cancer in the pediatric population, a topic with limited existing data, using a population-based cohort. Pediatric patients diagnosed with tongue cancer from 1975 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Survival rates were assessed using Kaplan-Meier analysis. Univariate survival analysis was conducted with the log-rank test, while multivariate analysis involved Cox proportional-hazards regression to identify factors influencing overall survival (OS). A predictive nomogram was developed based on Cox regression findings. In total, 97 pediatric patients with tongue cancer were identified, with a median age at diagnosis of 15 years (range: 1-19 years). Tumors were classified as squamous cell carcinoma (45.4%), rhabdomyosarcoma (RMS) (13.4%), and others (41.2%). Of the patients, squamous cell carcinoma was more common in older children, whereas rhabdomyosarcoma was more common in younger children. The Cox proportional hazard regression revealed that histology and surgery were significant independent predictors of overall survival. The chance of death increased with no surgery. Moreover, patients with squamous cell carcinoma or rhabdomyosarcoma have a poorer survival percentage than patients with other subtypes. Tongue cancer in children is rare and associated with poor survival outcomes. This study highlights the significance of tumor histology and surgical intervention in determining overall survival, offering valuable insights for clinical decision-making in pediatric tongue cancer.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study.

Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

A novel log odds of positive lymph nodes–based nomogram for predicting overall survival in patients with colorectal signet ring cell carcinoma: a SEER population-based study

PurposeConsidering the poor prognosis and high lymph node (LN) involvement rate of colorectal signet ring cell carcinoma (SRCC), this study aimed to construct a prognostic nomogram to predict overall survival (OS) with satisfactory accuracy and utility, based on LN status indicators with superior predictability.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we obtained cases of colorectal SRCC patients and employed univariate and multivariate Cox analyses to determine independent prognostic factors. Kaplan–Meier curves were utilized to visualize survival differences among these factors. Receiver operating characteristic curves were generated to assess predictive performances of models incorporating various LN status indicators. A novel nomogram, containing optimal LN status indicators and other prognostic factors, was developed to predict OS, whose discriminatory ability and accuracy were evaluated using calibration curves and decision curve analysis.ResultsA total of 1663 SRCC patients were screened from SEER database. Older patients and those with grades III–IV, tumor sizes > 39 mm, T3/T4 stage, N1/N2 stage, M1 stage, and higher log odds of positive lymph nodes (LODDS) values exhibited poorer prognoses. Age, grade, tumor size, TNM stage, and LODDS were independent prognostic factors. The model containing N stage and LODDS outperformed the one relying solely on N stage as LN status indicator, resulting in a validated nomogram for accurately predicting OS in SRCC patients.ConclusionThe integration of LODDS, N stage, and other risk factors into a nomogram offered precise OS predictions, enhancing therapeutic decision-making and tailored follow-up management for colorectal SRCC patients.

Multiple primary tumors in patients with surgically treated pancreatic cancer: a SEER population-based study.

With improving survival after pancreatic cancer (PC) resection, questions emerge concerning risk and patterns of metachronous tumors. We aimed to determine the incidence of multiple primary cancers among postoperative PC survivors. Patients undergoing PC surgery from 1975 to 2020 were identified in the Surveillance, Epidemiology, and End Results (SEER) registry. Standardized incidence ratios (SIRs) compared observed-to-expected cancers based on U.S. population rates. Cumulative incidence of secondary tumors was analyzed with Cox regression and cancer-specific survival with Kaplan-Meier curves. Of 6,100 resected PC patients, 267 (4.38%) developed multiple cancers over 6.2 years median follow-up period. Subsequent malignancies showed a rising cumulative incidence extending beyond 5 years. Lung cancer was the predominant second primary in both males (n=36, SIR 1.87) and females (n=32, SIR 2.17). Prostate (n=33) and breast (n=25) cancers were also common. Risk varied by latency period and gender. Postoperative PC patients face a measurable risk for secondary cancers. Enhanced long-term surveillance has the potential to improve early detection and outcomes in this survivor population. Our data provides real-world evidence which could help inform surveillance guidelines in the future.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study

BackgroundThis study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis.MethodsIn the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA).ResultsIn total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients.ConclusionOur prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Development and validation of a nomogram for predicting the early death of anaplastic thyroid cancer: a SEER population-based study.

Anaplastic thyroid cancer (ATC) is a highly aggressive malignancy with dismal prognosis. This study aimed to identify the independent risk factors and construct a readily-to-use nomogram to predict the probability of early death in ATC patients. Patients diagnosed with ATC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study for model development and internal validation. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for early death of ATC. Nomograms for predicting the probability of all-cause early death (ACED) and cancer-specific early death (CSED) of ATC were subsequently developed. The performance of the nomograms was comprehensively evaluated and validated in an internal cohort. A total of 696 ATC patients were included in this study, of which 488 patients in the training cohort and 208 patients in the validation cohort. The univariate and multivariate logistic regression analyses identified five independent factors (tumor size, M stage, surgery, radiotherapy and chemotherapy) in the ACED model and six variables in the CSED (gender, tumor size, M stage, surgery, radiotherapy and chemotherapy) model for the establishment of the nomograms. Calibration curves and receiver operating characteristic (ROC) curves showed satisfactory efficacy and consistency both in the training (ACED: AUC values: 0.814 (0.776-0.852); CSED: 0.778 (0.736-0.820)) and validation sets (ACED: 0.762 (0.696-0.827); CSED: 0.745 (0.678-0.812)). In addition, the decision curve analysis (DCA) demonstrated the favorable potential of the two nomograms in clinical application. The two nomograms assist clinicians to identify risk factors and predict the early death probability among ATC patients, thus guide individualized treatment to improve the prognosis.

Incidence, survival, and prognostic factors for patients with gastrointestinal mixed neuroendocrine non-neuroendocrine neoplasms: a SEER population-based study.

Mixed neuroendocrine non-neuroendocrine neoplasms (MiNENs) are a group of rare tumors with limited research currently available. This study aimed to analyze the incidence, survival, and prognostic factors of gastrointestinal MiNENs. We included data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. We compared the clinicopathologic characteristics and survival rates between MiNENs and neuroendocrine tumors (NETs), and calculated the incidence of MiNENs. We utilized univariate and multivariate Cox analysis to assess independent factors of prognosis and established a nomogram to predict 1-, 2-, and 3-year cancer-specific survival (CSS). Calibration and receiver operating characteristic (ROC) curves were drawn to validate the accuracy and reliability of the model. Decision curve analysis (DCA) was used to assess the clinical utility of the model. Patients with gastrointestinal MiNENs had a poorer prognosis than those with NETs. The overall incidence of gastrointestinal MiNENs has been increasing annually. Multivariate Cox regression analysis revealed that tumor size, lymph node metastasis, distant metastasis, and surgery were independent risk factors for CSS in MiNENs patients. Based on these risk factors, the 1-, 2-, and 3-year CSS nomogram model for MiNENs patients was established. Calibration, ROC, and DCA curves of the training and validation sets demonstrated that this model had good accuracy and clinical utility. Gastrointestinal MiNENs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in MiNENs patients, which may benefit clinical decision-making.

Prognostic factors for competing risk in patients with AIDS-related Kaposi's sarcoma: A SEER population-based study.

Despite the improved survival of patients with AIDS and Kaposi's sarcoma (KS), competing events are a non-negligible issue affecting the survival of such patients. In this study, we explored the prognostic factors of KS-specific and non-KS-specific mortality in patients with AIDS-related KS (AIDS-KS), accounting for competing risk. We identified 17 103 patients with AIDS-KS aged 18-65 years between 1980 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) 18 registry database. Prognostic factors for KS-specific and non-KS-specific mortality were determined by the Fine and Grey proportional subdistribution hazard model. We built competing risk nomograms and assessed their predictive performance based on the identified prognostic factors. In total, 12 943 (75.68%) patients died, 1965 (15.50%) of whom died from competing events. The KS-specific mortality rate was 14 835 per 100 000 person-years, and the non-KS specific mortality rate was 2719 per 100 000 person-years. Specifically, age >44 years was associated with an 11% decrease in the subdistribution hazard of KS-specific mortality compared with age <43 years but a 50% increase in the subdistribution hazard of non-KS-specific mortality. Being male was associated with a 26% increase in the subdistribution hazard of KS-specific mortality compared with being female but a 32% decrease in the subdistribution hazard of non-KS-specific mortality. Notably, being in the antiretroviral therapy (ART) era consistently showed a decrease in the subdistribution hazard of both KS-specific and non-KS-specific mortality than being in the pre-ART era. Competing events commonly occurred among patients with AIDS-KS, which deserves further attention to improve the prognosis of these patients.

A practical nomogram and risk stratification system for predicting survival outcomes in neuroblastoma patients: a SEER population-based study

BackgroundNeuroblastoma (NB) is a childhood malignancy with marked heterogeneity, resulting in highly variable outcomes among patients. This study aims to establish a novel nomogram and risk stratification system to predict the overall survival (OS) for patients with NB.MethodsWe analyzed neuroblastoma patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The nomogram was constructed using independent risk factors for OS, identified through univariate and multivariate Cox regression analyses. The accuracy of this nomogram was evaluated with the concordance index, receiver operating characteristic curve, calibration curve, and decision curve analysis. In addition, we developed a risk stratification system based on the total score of each patient in the nomogram.ResultsA total of 2185 patients were randomly assigned to the training group and the testing group. Six risk factors, including age, chemotherapy, brain metastases, primary site, tumor stage, and tumor size, were identified in the training group. Using these factors, a nomogram was constructed to predict 1-, 3-, and 5-year OS of NB patients. This model exhibited superior accuracy in the training and testing groups, exceeding traditional tumor stage prediction. Subgroup analysis suggested worse prognosis for retroperitoneal origin in the intermediate-risk group and adrenal gland origin in the high-risk group compared to other sites. Additionally, the prognosis for high-risk patients significantly improved after surgery. We also developed a web application to make the nomogram more user-friendly in clinical practices.ConclusionThis nomogram demonstrates excellent accuracy and reliability, offering more precise personalized prognostic predictions to clinical patients.

Prediction of distant organ metastasis and overall survival of lung cancer patients: a SEER population-based cohort study.

Distant organ metastasis is a common event in lung cancer (LC). However, the preferential metastatic pattern of different pathological types of LC and its effect on prognosis have not been comprehensively elucidated. This study aimed to explore the distant metastasis pattern and construct nomograms predicting the metastasis and survival of LC patients using the Surveillance, Epidemiology, and End Results (SEER) database. LC data were downloaded from the SEER database to conduct logistic regression and investigate risk factors for developing organ metastasis. A Cox regression analysis was conducted to investigate prognostic factors of LC. A Kaplan-Meier analysis was used to estimate overall survival outcomes. Nomograms were constructed to predict the probability of organ metastasis and the 1-, 3- and 5-year survival probability of LC patients. Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of the nomograms. All statistical analyses were conducted within R software. The liver is the most common metastatic organ of small cell carcinoma. The brain is the most likely metastasis site of large cell carcinoma, and bone is the most likely metastasis site for squamous cell carcinoma and adenocarcinoma. Patients with triple metastases (brain-bone-liver) have the worst prognosis, and for nonsquamous carcinoma with single organ metastasis, liver metastases conferred the worst prognosis. Our nomograms based on clinical factors could predict the metastasis and prognosis of LC patients. Different pathological types of LC have different preferential metastatic sites. Our nomograms showed good performance in predicting distant metastasis and overall survival. These results will provide a reference for clinicians and contribute to clinical evaluations and individualized therapeutic strategies.

Clinical subtypes and survival outcomes in invasive breast cancer with Paget’s disease: A SEER population-based study.

e12564 Background: The objective of this study was to investigate the clinicopathological characteristics, especially subtypes and survival outcomes of Invasive breast cancer with Paget's disease. Methods: 4930 patients were identified using the histopathology codes from the International Classification of Disease for Oncology third edition (ICD-O-3), from 2000 to 2019 in the Surveillance, Epidemiology, and End Results (SEER) database. Excluding 787 patients whose ICD-O-3 and TNM stages did not match, there were 663 patients with '8540/3, Paget disease, mammary', 976 patients with ‘8543/3, Paget disease and intraductal carcinoma(DCIS)’ and 2540 patients with ‘8541/3, and ‘8541/3, Paget disease and infiltrating ductal carcinoma (IDC) of breast’. ER and PR status have been available since 1990, and HER2 status since 2010. Results: Among patients with IDC and Paget's disease, 24.6% were under the age of 50 and 2.7% were male, which was higher than the other two groups. Expressions of ER and PR were higher than both groups, and HER2 expression was lower. The ratio of HR-/HER2+ subtype was also lower than the other two groups at 29.6%. ER+, Paget 43.7%, DCIS with Paget 36.4%, IDC with Paget 53.6%. PR+ Paget 21.6%, DCIS with Paget 21.7%, IDC with Paget 40.1%. HER2+ Paget 69.2%, DCIS with Paget 83.1 %, IDC with Paget 58.3%. HR-/HER2+subtype, Paget 40.7%, DCIS with Paget 49.2%, IDC with Paget 29.6%. As a result of survival analysis in patients with IDC and Paget's disease, the HR-/HER2+ subtype had shown the best prognosis in Overall Survival (OS) analysis (5year OS, HR+/HER2- 70.8%, HR+/HER2+ 74.9%, HR-/HER2- 63.7%, HR-/HER2+ 80.7% P=0.010) In the BCSS analysis, the 5-year BCSS was the best at 87.0% in the HR-HER2+ subtype, but there was no statistically significant difference. Conclusions: In IDC with Paget's disease, the distribution of subtypes is different from that of DCIS with Paget's, and HER2 expression can be considered as a favor prognostic factor. It is considered necessary to study the relationship between the loss of HER2 overexpression and invasion/progression in invasive breast cancer with Paget's disease.