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

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

Radiotherapy can improve overall survival in patients with lymph-node positive, high-grade neuroendocrine cervical cancer: construction of two prognostic nomograms to predict treatment outcome.

Background: To explore the beneficial subgroups after radiotherapy in high-grade neuroendocrine cervical cancer (HGNECC) and construct two survival prognosis models to quantify the efficacy of radiotherapy assessment. Methods: In this retrospective study, we included 592 eligible samples from the Surveillance, Epidemiology, and End Results (SEER) database and 56 patients with lymph-node positive HGNECC from Chongqing Medical University. Cox regression analysis was used to identify independent survival prognosis risk factors for HGNECC patients. Propensity score matching (PSM) was employed as it balances the baseline differences among grouping methods. Kaplan-Meier (K-M) curves were used to analyze survival differences among different groups. Two survival prediction nomograms were constructed separately (using the "rms" package in R software) based on whether radiotherapy was administered. The stability and accuracy of these models were assessed using receiver operating characteristic (ROC) curves and calibration curves in both the training and validation datasets. P<0.05 was considered to indicate statistically significant differences. Results: Age, Federation of Gynecology and Obstetrics (FIGO)-stage, and treatment methods (surgery vs. chemotherapy) were independent risk factors that affected survival prognosis (P<0.05). Radiotherapy showed adverse effects on survival in patients with early tumor staging, lymph-node negative status, and absence of distant metastasis (all P<0.05). The lymph-node positive group had a beneficial response to radiotherapy (P<0.05), and patients with metastasis in the radiotherapy group showed a survival protection trend (P=0.069). Conclusion: In HGNECC, patients with lymph-node positive status can benefit from radiotherapy in terms of survival outcomes. We constructed two survival prediction models based on whether radiotherapy was administered, thereby offering a more scientifically guided approach to clinical treatment planning by quantifying the radiotherapy efficacy.

Demographics, Histopathology, and Treatment Outcomes of Squamous Cell Carcinoma of the Prostate.

BACKGROUND: Squamous cell carcinoma of the prostate (SCCP) is a neoplasm that comprises fewer than 1% of all primary prostate cancer diagnoses. Given its rarity, there is a paucity of data regarding the treatment of this disease. The limited literature points to the potential of local therapy in conjunction with chemotherapy to improve patient mortality. METHODS: Using the National Cancer Initiative's Surveillance, Epidemiology, and End Results (SEER) database, a retrospective review of patients diagnosed with primary SCCP between 2000 and 2018 was performed. Patient demographics, tumor characteristics, and patient outcomes based on treatment modality were analyzed. Univariate and survival analyses were conducted with p < 0.05 indicating statistical significance. RESULTS: A total of 66 patients were identified. Five-year overall survival (5y OS) was 24%; mean and median survival were 2.2 years (1.8, 2.7) and 1.2 years (0.3, 2.1), respectively. Patients with Grade I or Grade II disease had an increased 5y OS of 55% (27%, 83%). In comparison, 5y OS was 13% (-2%, 29%) for patients with Grade III and Grade IV disease (p = 0.017). Analysis of 5y OS based on disease histology revealed patients with papillary SCC had a 5y OS of 50% [9.2%, 91%], compared to 21% [9%, 34%] for patients with SCC, not otherwise specified and 0% for those with lymphoepithelial carcinoma (p = 0.048). Analysis of 5y OS stratified by treatment modality revealed no statistically significant change with any treatment (surgery, radiotherapy, and chemotherapy). No difference in 5y OS was seen between those treated with radical prostatectomy versus external beam radiation therapy. CONCLUSIONS: The literature on SCCP remains sparse; the rarity of this disease limits analysis. While the investigation undertaken in this paper does not find any change in 5y OS regardless of treatment modality, the variation in 5y OS based on histologic classification of SCCP points to a potential route for the future treatment of this disease.

Development and validation of a nomogram to predict cancer-specific survival of patients with large hepatocellular carcinoma accepting surgical resection: a real-world analysis based on the SEER database.

Background: Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR. Methods: The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups. Results: In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram. Conclusions: The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.

Prognostic nomogram of overall survival for radiation therapy in hepatocellular carcinoma: a population study based on the SEER database and an external cohort.

Purpose: Radiotherapy (RT) plays an important role in the treatment of hepatocellular carcinoma (HCC). To screen patients who benefit most from RT, a nomogram for survival prediction of RT based on a large sample of patients with HCC was created and validated. Methods: A total of 2,252 cases collected from the Surveillance, Epidemiology, and End Results (SEER) database were separated into a training or an internal validation cohort in a 7:3 ratio (n = 1,565:650). An external validation cohort of cases from our institute was obtained (n = 403). LASSO regression and Cox analyses were adopted to develop a nomogram for survival prediction. The decision curve analysis (DCA), calibration curve, and time-dependent receiver operating characteristic curves (TROCs) demonstrated the reliability of the predictive model. Results: For patients with HCC who received RT, the analyses revealed that the independent survival prediction factors were T stage {T2 vs. T1, hazard ratio (HR) =1.452 [95% CI, 1.195-1.765], p < 0.001; T3 vs. T1, HR = 1.469 [95% CI, 1.168-1.846], p < 0.001; T4 vs. T1, HR = 1.291 [95% CI, 0.951-1.754], p = 0.101}, N stage (HR = 1.555 [95% CI, 1.338-1.805], p < 0.001), M stage (HR = 3.007 [95% CI, 2.645-3.418], p < 0.001), max tumor size (>2 and ≤5 vs. ≤2 cm, HR = 1.273 [95% CI, 0.992-1.633], p = 0.057; >5 and ≤10 vs. ≤2 cm, HR = 1.625 [95% CI, 1.246-2.118], p < 0.001; >10 vs. ≤2 cm, HR = 1.784 [95% CI, 1.335-2.385], p < 0.001), major vascular invasion (MVI) (HR = 1.454 [95% CI, 1.028-2.057], p = 0.034), alpha fetoprotein (AFP) (HR = 1.573 [95% CI, 1.315-1.882], p < 0.001), and chemotherapy (HR = 0.511 [95% CI, 0.454-0.576], p < 0.001). A nomogram constructed with these prognostic factors demonstrated outstanding predictive accuracy. The area under the curve (AUC) in the training cohort for predicting overall survival (OS) at 6, 12, 18, and 24 months was 0.824 (95% CI, 0.803-0.846), 0.824 (95% CI, 0.802-0.845), 0.816 (95% CI, 0.792-0.840), and 0.820 (95% CI, 0.794-0.846), respectively. The AUCs were similar in the other two cohorts. The DCA and calibration curve demonstrated the reliability of the predictive model. Conclusion: For patients who have been treated with RT, a nomogram constructed with T stage, N stage, M stage, tumor size, MVI, AFP, and chemotherapy has good survival prediction ability.

Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis.

Background: This study aimed to develop and validate nomograms to predict overall survival (OS) for pelvic Ewing's sarcoma (EWS) and chordoma, identify prognostic factors, and compare outcomes between the two conditions. Methods: We identified patients diagnosed with pelvic EWS or chordoma from the SEER database (2001-2019). Independent risk factors were identified using univariate and multivariate Cox regression analyses, and these factors were used to construct nomograms predicting 3-, 5-, and 10-year OS. Validation methods included AUC, calibration plots, C-index, and decision curve analysis (DCA). Kaplan-Meier curves and log-rank tests compared survival differences between low- and high-risk groups. Results: The study included 1175 patients (EWS: 611, chordoma: 564). Both groups were randomly divided into training (70 %) and validation (30 %) cohorts. OS was significantly higher for chordoma. Multivariate analysis showed year of diagnosis, income, stage, and surgery were significant for EWS survival, while age, time to treatment, stage, and surgery were significant for chordoma survival. Validation showed the nomograms had strong predictive performance and clinical utility. Conclusions: The nomograms reliably predict overall survival (OS) in pelvic EWS and chordoma, helping to identify high-risk patients early and guide preventive measures. The study also found that survival rates are significantly higher for chordoma, highlighting different prognostic profiles between EWS and chordoma.

Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients.

BACKGROUND: HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients. METHODS: 240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA). RESULTS: The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model. CONCLUSION: In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.

Survival prediction in peritoneal mesothelioma: a nomogram based on SEER data and a Chinese cohort.

Objective: This study aimed to develop nomogram predicting overall survival (OS) of patients with peritoneal mesothelioma (PeM) using data from Surveillance, Epidemiology, and End Results (SEER) database and a Chinese institution. Methods: 1,177 PeM patients from the SEER database were randomized into training and internal validation cohorts at a 7:3 ratio. An external validation cohort consisting of 109 patients was enrolled from a Chinese institution. Nomogram was constructed based on variables identified through multivariate Cox regression analysis and evaluated by consistency indices (C-index), calibration plots, and receiver operating characteristic (ROC) curves. Patients were stratified into different risk categories, and Kaplan-Meier survival analysis was used to assess OS differences among these groups. Results: The nomogram, incorporating age, gender, histological type, T stage, M stage, and surgical status, demonstrated strong predictive capability with C-index values of 0.669 for the training cohort, 0.668 for the internal validation cohort, and 0.646 for the external validation cohort. The nomogram effectively stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly poorer OS (P < 0.05). Multivariate analysis confirmed gender, age, surgical intervention, and M stage as independent prognostic factors (P < 0.05). Specifically, male gender, older age, and unspecified M stage were linked to worse outcomes, while surgical intervention was associated with improved survival. Conclusion: The nomogram provide a reliable tool for predicting the survival in PeM patients, facilitating more informed treatment decisions. Key independent prognostic factors include gender, age, surgical intervention, and M stage.

Nomograms to predict lung metastasis in malignant primary osseous spinal neoplasms and cancer-specific survival in lung metastasis subgroup.

Purpose: To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis and predicting cancer-specific survival (CSS) in the lung metastasis subgroup. Methods: A total of 1,298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing sarcoma, and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3- and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms. Results: Histologic type, grade, lymph node involvement, tumor size, tumor extension, and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827, respectively. Age, histologic type, surgery at primary site, and grade were identified as the prognostic factors for the CSS. The AUC for the 3- and 5-year CSS were 0.790 and 0.740, respectively. Calibration curves revealed good agreements, and the Hosmer and Lemeshow test identified the models to be well fitted. DCA curves demonstrated that nomograms were clinically useful. Conclusion: The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis and make a personalized CSS evaluation for the lung metastasis subgroup.

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different machine learning algorithms were applied to develop treatment options for predicting patients with prostate cancer, differentiating between surgical and non-surgical treatments. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value. The Shapley Additive Explanations (SHAP) method was employed to investigate the key factors influencing the prediction process. Survival analysis methods were used to compare the survival rates of different treatment options. The CatBoost model yielded the best results (AUC = 0.939, sensitivity = 0.877, accuracy = 0.877). SHAP interpreters revealed that the T stage, cancer stage, age, cores positive percentage, prostate-specific antigen, and Gleason score were the most critical factors in predicting treatment options. The study found that surgery significantly improved survival rates, with patients undergoing surgery experiencing a 20.36% increase in 10-year survival rates compared with those receiving non-surgical treatments. Among surgical options, radical prostatectomy had the highest 10-year survival rate at 89.2%. This study successfully developed a predictive model to guide treatment decisions for prostate cancer. Moreover, the model enhanced the transparency of the decision-making process, providing clinicians with a reference for formulating personalized treatment plans.

A novel nomogram based on the number of positive lymph nodes can predict the overall survival of patients with pancreatic head cancer after radical surgery.

BACKGROUND: This study aimed to construct a novel nomogram based on the number of positive lymph nodes to predict the overall survival of patients with pancreatic head cancer after radical surgery. MATERIALS AND METHODS: 2271 and 973 patients in the SEER Database were included in the development set and validation set, respectively. The primary clinical endpoint was OS (overall survival). Univariate and multivariate Cox regression analyses were used to screen independent risk factors of OS, and then independent risk factors were used to construct a novel nomogram. The C-index, calibration curves, and decision analysis curves were used to evaluate the predictive power of the nomogram in the development and validation sets. RESULTS: After multivariate Cox regression analysis, the independent risk factors for OS included age, tumor extent, chemotherapy, tumor size, LN (lymph nodes) examined, and LN positive. A nomogram was constructed by using independent risk factors for OS. The C-index of the nomogram for OS was 0.652 [(95% confidence interval (CI): 0.639-0.666)] and 0.661 (95%CI: 0.641-0.680) in the development and validation sets, respectively. The calibration curves and decision analysis curves proved that the nomogram had good predictive ability. CONCLUSIONS: The nomogram based on the number of positive LN can effectively predict the overall survival of patients with pancreatic head cancer after surgery.

Treatment of secondary uterine malignancy following radiotherapy for cervical cancer: a study based on the SEER database.

BACKGROUND: Radiotherapy is one of the main treatments for cervical cancer. Long-term complications of radiation exposure include the emergence of secondary tumors. This is a retrospective study based on an American population. We discuss the optimal treatment modality for patients with radiation-induced secondary uterine malignancy based on the Surveillance, Epidemiology, and End Results database. METHODS: The study included patients with a definitive pathological diagnosis of cervical cancer who were diagnosed with a uterine malignant tumor ≥ 1 year later. Patients in whom cervical cancer was not the first tumor or patients with missing data were excluded. Univariate and multivariate analyses were performed using the COX regression model to screen independent prognostic factors affecting overall survival. Kaplan-Meier survival curves were analyzed using the R software package. RESULTS: We screened 142 patients with a secondary uterine malignancy after cervical cancer treatment, 115 patients with a secondary uterine malignancy after radiotherapy, and 27 patients with a secondary uterine malignancy who did not receive radiotherapy. The average latency period for developing a secondary tumor was 8 years, and 57.04% of the patients had a second tumor at ≥ 60 years of age. In patients with a secondary uterine malignancy after radiotherapy, surgery improved the prognosis [hazard ratio (HR), 0.374; 95% confidence interval (CI), 0.229-0.612], whereas radiotherapy and chemotherapy did not reduce the risk of death. In the subgroup analysis, the surgery plus chemotherapy group had a significantly better survival prognosis than the other groups (HR, 0.251; 95% CI, 0.122-0.515). CONCLUSIONS: The results suggest that the treatment modality in patients with secondary uterine malignancy after radiotherapy for cervical cancer has a significant impact on survival. The survival outcomes of patients receiving surgery combined with chemotherapy are superior to those of patients receiving other treatments.

Pelvic organ-preserving radical cystectomy versus standard radical cystectomy in female patients diagnosed with bladder cancer.

BACKGROUND: Pelvic organ-preserving radical cystectomy (POPRC) has been reported to result in a better postoperative quality of life in female with bladder cancer compared to standard radical cystectomy (SRC). However, its oncological outcomes remain a concern. PATIENTS AND METHODS: Female patients with bladder cancer who underwent POPRC or SRC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Logistic regression was used to identify predictors of POPRC usage. To avoid the potential impact of baseline differences between groups on survival, a 1:2 propensity score matching (PSM) was implemented. After that, Kaplan-Meier curves and Log-rank tests were used to determine the significance of overall survival (OS) differences between patients in the SRC group and POPRC group. Finally, subgroup analysis based on predetermined indicators was performed. RESULTS: A total of 2193 patients were included with a median follow-up of 53 months, of whom 233 (10.6%) received POPRC and 1960 (89.4%) received SRC. No definitive predictors of POPRC were identified. Before PSM, POPRC resulted in comparable OS to SRC (HR = 1.09, p = 0.309), while after PSM, POPRC was associated with significantly worse OS (HR = 1.23, p = 0.038). In subgroup analyses, POPRC led to non-inferior OS (HR = 1.18, 95%CI 0.71-1.95, p = 0.531) in patients with non-muscle invasive bladder cancer (NMIBC) and T2 patients (HR = 1.07, p = 0.669), but significantly worse OS in T3 patients (HR = 1.41, p = 0.02). CONCLUSION: Currently, patients undergoing POPRC have not undergone strict screening, and candidates for POPRC should have more stringent criteria in the future to achieve satisfactory oncological outcomes. However, flaws in the study make more evidence needed to support our findings.

Long term clinical outcomes of cervical cancer patients who were recommended surgery but did not undergo it: A SEER database study.

BACKGROUND: This study analyzed the long-term clinical outcomes of cervical cancer patients recommended surgery but who did not undergo it using the Surveillance, Epidemiology, and End Results (SEER) database. The aim was to identify the subgroups with comparable overall survival (OS) and cancer-specific survival (CSS) through stratified analysis. METHODS: Cases of cervical cancer were retrieved from SEER database using SEER*Stat software. This included patients in the non-surgery group (recommended surgery but did not undergo it), and a reference surgery group. Propensity score matching balanced differences between the non-surgery and surgery groups. Stratified analysis and log-rank tests were used to identify subgroups within the non-surgery group with comparable OS and CSS to the surgery group. RESULTS: A total of 30,807 cervical cancer patients were included in the OS and CSS analysis. In the matched cohort (n = 1278), patients in the non-surgery group had significantly lower 5-year CSS (63.2 % vs. 80.1 %, P < 0.001) and 5-year OS (59.0 % vs. 78.0 %, P < 0.001). However, within the matched cohort, there was no statistically significant difference in OS and CSS between the non-surgery and surgery groups in subgroups diagnosed during 2010-2014 (P = 0.064, P = 0.182), 2015-2020 (P = 0.122, P = 0.518), T2 stage (P = 0.139, P = 0.052), T3 stage (P = 0.502, P = 0.317), or with distant metastasis (M1) (P = 0.411, P = 0.520). CONCLUSION: Patients in the non-surgery group generally exhibited lower long-term clinical outcomes compared to those in the surgery group. However, with advancements in non-surgical treatment techniques, particularly notable in patients with T2, T3, and M1 stages, these differences are gradually diminishing.

Suicide among patients with oral cancer: A population-based study.

BACKGROUND: Patients with oral cancer usually experience disfigurement and dysfunction which are shared risk factors of suicide. The aim of the study was to comprehensively assess the characteristics of suicide and risk factors for suicide in patients with oral cancer. METHODS: Surveillance, Epidemiology, and End Results database was used to acquire information of patients with common malignant tumors including oral cancer from 1975 to 2020. The aim was to explore the incidence of suicide, and timing of suicide among patients with oral cancer. A Fine-Gray competing risks regression model was employed to analyze risk factors associated with suicide among patients with various demographic and tumor characteristics. RESULTS: Totally, 7685 patients with different malignant tumors committed suicide. Among them, 203 patients with oral cancer died due to suicide, presenting a suicide rate of 54.5/100,000 person-years, which was almost 3.5 times that of the US general population and 1.5 times that of the overall US patients with cancer in our study. Approximately 18 %, 40 %, and 55 % of suicides occurred in first year, first 3 years, and first 5 years after diagnosis. Being male, White race, and having a single primary tumor might be regarded as the risk factors for suicide. CONCLUSION: As oral cavity is closely associated with appearance, pronunciation and ingestion, patients with oral cancer have a significant high risk of suicide. Tremendous attention needs to be paid to patients with oral cancer particularly those exhibiting characteristics associated with a high risk of suicide.

The contribution of rural/urban residence to incidence and survival in thymoma and thymic carcinoma, a retrospective cohort study of the SEER 2000-2020 database.

OBJECTIVE: Rural-urban healthcare disparities have been demonstrated throughout the United States, particularly in acquiring oncologic care. In this study, we aim to discern the role of rural-urban health disparities in thymic cancer incidence and uncover potential survival disparities. METHODS: The Surveillance, Epidemiology, and End Results (SEER) 17-State database was queried for all cases of thymoma (ICD-O-3/3 codes: 8580-8585) and thymic carcinoma (8586) located in the thymus (primary site code C37.9) diagnosed between 2000 and 2020. Residence was established using SEER Rural-Urban Continuum Codes. Incidence trend modeling for rural versus urban patients was completed using Joinpoint Regression Software. Chi-square, Kaplan-Meier with log-rank testing, and Cox proportional hazards was completed using SPSS, with significance set to p <0.05. RESULTS: Joinpoint analysis revealed a significant growth in incidence in the urban population compared to a stagnant incidence among the rural population. Disease specific survival was higher among urban patients on univariate modeling (p = 0.010), and confirmed on multivariate analysis, whereby rural living conferred an adjusted hazard ratio of 1.263 (95 % CI 1.045-1.527; p = 0.016) in comparison to urban patients. CONCLUSIONS: These findings demonstrate differences between thymic cancer incidence and outcomes in patients living in urban versus rural environments and demonstrate an important disparity.

Trends in HR+ metastatic breast cancer survival before and after CDK4/6 inhibitor introduction in the United States: a SEER registry analysis of patients with HER2- and HER2+ metastatic breast cancer.

PURPOSE: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have improved patient survival in hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) metastatic breast cancer (mBC) in clinical trials and real-world studies. However, investigations of survival gains in broader HR+/HER2- mBC populations using epidemiological approaches are limited. METHODS: This retrospective study used SEER registry data to assess breast cancer-specific survival (BCSS) in patients diagnosed with HR+/HER2- de novo mBC from 2010 to 2019. Kaplan-Meier and Cox proportional hazards models were used to compare BCSS in patients diagnosed before (2010‒2013 with follow-up to 2014) and after (2015‒2018 with follow-up to 2019) the 2015 guideline recommendations for CDK4/6i use. A comparison was made to patients with HR+/HER2-positive (HER2+) de novo mBC, for which no major guideline changes occurred during 2015-2018. RESULTS: Data from 11,467 women with HR+/HER2- mBC and 3260 women with HR+/HER2+ mBC were included. After baseline characteristic adjustment, patients with HR+/HER2- mBC diagnosed post-2015 (n = 6163), had an approximately 10% reduction in risk of BC-specific death compared with patients diagnosed pre-2015 (n = 5304; HR = 0.895, p < 0.0001). Conversely, no significant change was observed in HR+/HER2+ BCSS post-2015 (n = 1798) versus pre-2015 (n = 1462). Similar results were found in patients aged ≥ 65 years. CONCLUSION: Using one of the largest US population-based longitudinal cancer databases, significant improvements in BCSS were noted in patients with HR+/HER2- mBC post-2015 versus pre-2015, potentially due to the introduction of CDK4/6i post-2015. No significant improvement in BCSS was observed in patients with HR+/HER2+ mBC post-2015 versus pre-2015, likely due to the availability of HER2-directed therapies in both time periods.