Efficacy of cytoreductive surgery for metastatic upper tract urothelial carcinoma: a Surveillance, Epidemiology and End Results (SEER) study of 508 patients.

Background: Cisplatin-based combination chemotherapy alone is currently considered the standard of care for patients with metastatic upper tract urothelial carcinoma (mUTUC). However, less research has been done on the efficacy of other combinations. In this study, we explored the role of cytoreductive surgery in patients with mUTUC receiving different types of systemic therapy. Methods: Data from 9,436 anonymized records were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2008-2018. Of these, 508 individuals received systemic therapy subsequent to being diagnosed with mUTUC. These patients had all been treated with systemic therapies such as chemotherapy and/or radiotherapy. Patients were stratified into either a non-surgical or surgical group based on cytoreductive surgery status before systemic therapeutics commenced. Kaplan-Meier curves were used to compare overall survival (OS) and cancer-specific survival (CSS). Cox's proportional hazard models were then used to analyze prognostic factors related to OS and CSS. Results: Of the 508 cases, 36.8% (n=187) had received cytoreductive surgery with systemic treatments. The remaining 63.2% (n=321) received either chemotherapy and/or radiotherapy alone. Kaplan-Meier curves showed that 11.6% had 3-year OS [95% confidential interval (CI): 7.1-17.3] for cytoreductive surgery with systemic treatment and 4.9% (95% CI: 2.7-8.0) for systemic treatment alone (P=0.001). The 3-year CSS was 14.9% for cytoreductive surgery plus systemic treatment (95% CI: 9.4-21.7%) and 6.0% (95% CI: 3.4-9.8%) for systemic treatments alone (P=0.003). Under multivariate regression analysis, primary ureter site OS had a hazard ratio (HR) of 0.74 (95% CI: 0.58-0.95, P=0.02) and a CSS HR of 0.72 (95% CI: 0.56-0.94, P=0.01). The cytoreductive surgery OS HR was 0.79 (95% CI: 0.65-0.95, P=0.02) and the CSS HR was 0.75 (95% CI: 0.61-0.92, P=0.006). Additionally, chemotherapy had an OS HR of 0.46 (95% CI: 0.33-0.0.65, P<0.001) and a CSS HR of 0.44 (95% CI: 0.31-0.63, P<0.001). Bones and liver metastases were also indicative of poorer prognosis. Validation was conducted through subgroup analysis which suggested cytoreductive surgery was effective only for patients who received chemotherapy or combined chemo-radiotherapy but not for radiotherapy alone. Conclusions: Cytoreductive surgery provided significantly increased OS and CSS for mUTUC patients who received chemotherapy or combined chemo-radiotherapy in this study. In addition, the primary tumor and metastatic sites were shown to be related to improved patient survival although this was a small and relatively homogeneous cohort of study, sample therefore, further research is required.

A machine learning clinic scoring system for hepatocellular carcinoma based on the Surveillance, Epidemiology, and End Results database.

Background: Hepatocellular carcinoma (HCC) poses a global threat to life; however, numerical tools to predict the clinical prognosis of these patients remain scarce. The primary objective of this study is to establish a clinical scoring system for evaluating the overall survival (OS) rate and cancer-specific survival (CSS) rate in HCC patients. Methods: From the Surveillance, Epidemiology, and End Results (SEER) Program, we identified 45,827 primary HCC patients. These cases were randomly allocated to a training cohort (22,914 patients) and a validation cohort (22,913 patients). Univariate and multivariate Cox regression analyses, coupled with Kaplan-Meier methods, were employed to evaluate prognosis-related clinical and demographic features. Factors demonstrating prognostic significance were used to construct the model. The model's stability and accuracy were assessed through C-index, receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curve analysis (DCA), while comparisons were made with the American Joint Committee on Cancer (AJCC) staging. Ultimately, machine learning (ML) quantified the variables in the model to establish a clinical scoring system. Results: Univariate and multivariate Cox regression analyses identified 11 demographic and clinical-pathological features as independent prognostic indicators for both CSS and OS using. Two models, each incorporating the 11 features, were developed, both of which demonstrated significant prognostic relevance. The C-index for predicting CSS and OS surpassed that of the AJCC staging system. The area under the curve (AUC) in time-dependent ROC consistently exceeded 0.74 in both the training and validation sets. Furthermore, internal and external calibration plots indicated that the model predictions aligned closely with observed outcomes. Additionally, DCA demonstrated the superiority of the model over the AJCC staging system, yielding greater clinical net benefit. Ultimately, the quantified clinical scoring system could efficiently discriminate between high and low-risk patients. Conclusions: A ML clinical scoring system trained on a large-scale dataset exhibits good predictive and risk stratification performance in the cohorts. Such a clinical scoring system is readily integrable into clinical practice and will be valuable in enhancing the accuracy and efficiency of HCC management.

21-Gene Recurrence Score and Survival Outcomes in the Phase III Multicenter TAILORx Clinical Trial.

BACKGROUND: Recurrence score (RS) based on a 21-gene genomic assay is frequently used to estimate risk of distant recurrence for choice of adjuvant chemotherapy in breast cancer. It remains unclear whether RS is an independent prognostic factor for breast cancer-specific survival (BCSS) and overall survival (OS) in the TAILORx trial population. METHODS: We evaluated the association of RS with BCSS and OS plus recurrence-free interval (RFI) and invasive disease-free survival (DFS) using multivariable Cox proportional hazards regression analysis, adjusting for clinicopathologic measures, in 8,916 patients with hormone receptor-positive, HER2-negative, node-negative breast cancer. Likelihood ratio (LR) test was used to assess the relative amount of prognostic information provided by RS to BCSS, OS, RFI, and DFS, comparatively. RESULTS: Event rates for BCSS, OS, RFI, and DFS were 1.7%, 5.2%, 5.6%, and 12.6%, respectively, by up to 11.6 years of follow-up. Compared with low-range RS (0-10), patients with midrange (11-25) and high-range (26-100) RS had inferior BCSS (adjusted hazard ratio [aHR], 5.12 [95% CI, 2.09-16.92] and 8.03 [95% CI, 2.91-28.47], respectively) and RFI (aHR, 1.68 [95% CI, 1.23-2.36] and 3.05 [95% CI, 2.02-4.67], respectively), independent of clinicopathologic factors. High-range score was associated with an increased risk of DFS (aHR, 1.56 [95% CI, 1.20-2.04]) but not significantly associated with OS (aHR, 1.44 [95% CI, 0.95-2.18]). Midrange score was associated with neither DFS (aHR, 1.15 [95% CI, 0.96-1.38]) nor OS (HR 1.14 [95% CI, 0.87-1.52]). LR-χ2 values were 83.0 and 65.1 for RFI and BCSS, respectively, and 17.5 and 33.6 for OS and DFS, respectively (P<.0001). CONCLUSIONS: RS is an independent measure for BCSS and recurrence prognoses relative to OS in early-stage breast cancer. It carries more prognostic information for breast cancer-specific outcomes.

A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database.

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.

Development and validation of nomograms for predicting survival in small cell lung cancer patients with brain metastases: a SEER population-based analysis.

OBJECTIVE: To develop prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS) probabilities in small cell lung cancer (SCLC) patients with brain metastasis (BM). METHODS: SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015) were randomly allocated to training (n=1771) and validation (n=757) cohorts. Independent prognostic factors for OS and CSS were determined using univariate and multivariate Cox regression analyses in the training cohort, and prognostic nomograms for OS and CSS were constructed based on these factors. The efficacy of the nomograms was assessed using area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI), with the TNM staging model as a comparator. RESULTS: Multivariate Cox analysis identified age, sex, race, tumor size, N staging, and presence of liver/bone/lung metastases, chemotherapy, and radiotherapy as independent prognostic factors for both OS and CSS. Prognostic nomograms were developed based on these factors. In both the training and validation cohorts, the AUC values of the nomograms for OS and CSS were significantly above 0.7, surpassing those for TNM staging. Calibration curves demonstrated a high degree of concordance between predicted and actual survival. The constructed nomograms showed superior clinical utility compared to the TNM staging system, as evidenced by NRI, IDI, and DCA. CONCLUSIONS: This retrospective study successfully developed and validated prognostic nomograms for SCLC patients with BM, providing valuable tools for oncologists to enhance prognosis evaluation and guide clinical decision-making.

Local treatment benefits patients with oligometastatic prostate cancer: A systematic review and meta-analysis.

OBJECTIVES: This study aims to evaluate the efficacy of local treatment (LT), including radiotherapy (RT) and cytoreductive prostatectomy (CRP), in improving outcomes for patients with oligometastatic prostate cancer (OmPCa). METHODS: A systematic review and meta-analysis of articles from PubMed, Embase, and Web of Science published between 2010 and November 2023 were conducted. The study included 11 articles, comprising three randomized controlled trials (RCTs) and eight retrospective analyses. The study assessed overall survival (OS), radiographic progression-free survival (rPFS), prostate-specific antigen (PSA) PFS, cancer-specific survival (CSS), and complication rate (CR). RESULTS: OS was significantly improved in the LT group, with both RCTs and non-RCTs showing statistical significance [hazard ratios (HR) = 0.64; 95% confidence intervals (95% CIs), 0.51-0.80; p < 0.0001; HR = 0.55; 95% CIs, 0.40-0.77; p = 0.0004]. For rPFS, RCTs did not show statistically significant outcomes (HR = 0.60; 95% CIs, 0.34-1.07; p = 0.09), whereas non-RCTs demonstrated significant results (HR = 0.42; 95% CIs, 0.24-0.72; p = 0.002). Both RCTs and non-RCTs showed a significant improvement in PSA-PFS (HR = 0.44; 95%CI, 0.29-0.67; p = 0.0001; HR = 0.51; 95% CIs, 0.32-0.81; p = 0.004). For CSS, RCTs demonstrated statistical differences (HR = 0.65; 95% CIs, 0.47-0.90; p = 0.009), whereas non-RCTs did not (HR = 0.61; 95% CIs, 0.29-1.27; p = 0.19). Regarding CR, the risk difference was -0.22 (95% CIs, -0.32 to -0.12; p < 0.00001). CONCLUSION: LT significantly improved OS and PFS in patients with OmPCa. Further RCTs are necessary to confirm these results.

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database.

Purpose: The objective of this investigation was to construct and validate a nomogram for prognosticating cancer-specific survival (CSS) in patients afflicted with gastrointestinal stromal tumor (GIST) at 3-, 5-, and 8-years post-diagnosis. Methods: Data pertaining to patients diagnosed with GIST were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Through random selection, a training cohort (70%) and a validation cohort (30%) were established from the patient population. Employing a backward stepwise Cox regression model, independent prognostic factors were identified. Subsequently, these factors were incorporated into the nomogram to forecast CSS rates at 3-, 5-, and 8-years following diagnosis. The nomogram's performance was assessed using indicators such as the consistency index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis (DCA). Results: This investigation encompassed a cohort of 3,062 GIST patients. By analyzing the Cox regression model within the training cohort, nine prognostic factors were identified: age, sex, race, marital status, AJCC (American Joint Committee on Cancer) stage, surgical status, chemotherapy status, radiation status, and income status. The nomogram was subsequently developed and subjected to both internal and external validation. The nomogram exhibited favorable discrimination abilities, as evidenced by notably high C-indices and AUC values. Calibration curves confirmed the nomogram's reliability. Moreover, the nomogram outperformed the AJCC model, as demonstrated by enhanced NRI and IDI values. The DCA curves validated the clinical utility of the nomogram. Conclusion: The present study has successfully constructed and validated the initial nomogram for predicting prognosis in GIST patients. The nomogram's performance and practicality suggest its potential utility in clinical settings. Nevertheless, further external validation is warranted.

The Prognostic Value of Human Papillomavirus Status in Penile Cancer: Outcomes From a Norwegian Cohort Study.

INTRODUCTION: Penile squamous cell carcinoma (PSCC) can develop from human papillomavirus (HPV) infection. This study investigates if the prognostic value of the TNM stage groups or the components tumor stage (pT), grade of differentiation (Grade), lymphovascular invasion (LVI), and nodular stage (pN) depend on HPV status. Also, whether the value of tumor parameters (pT, Grade, and LVI) for predicting node-positive disease depends on HPV status was investigated. PATIENTS AND METHODS: Stored tumor tissue from 226 patients treated for PSCC in Western Norway between 1973 and 2023 was investigated for HPV DNA. Histopathological variables were reevaluated according to the current TNM classification. Disease course was registered from hospital records. Inclusion of an interaction term between HPV and TNM stage groups in Cox regression enabled analysis of whether cancer-specific survival (CSS) of the stage groups depended on HPV status. This was also done separately for pT, Grade, LVI, and pN. Logistic regression with interaction terms between HPV and the tumor parameters were used to investigate if their predictive value depended on HPV status. RESULTS: HPV DNA was detected in 43% of the tumors. Stratified by HPV status, there was no significant interaction term in the Cox regression between HPV status and TNM stage groups (P = .74). Similar results were found for pT (P = .94), Grade (P = .08), LVI (P = .91) and pN (P = .77). Moreover, there were no significant interaction terms in the logistic regression between HPV status and the tumor parameters pT, Grade, and LVI (all P > .2). CONCLUSIONS: This study found that prognosis of the TNM stage groups and the components pT, Grade, LVI, and pN were not modified by HPV in PSCC. The value of pT, Grade, and LVI for predicting lymph node-positive disease was not affected by HPV status.

The Epidemiology of Dermatofibrosarcoma Protuberans Incidence, Metastasis, and Death Among Various Population Groups: A SEER Database Analysis.

BACKGROUND: Limited information exists regarding the epidemiology, metastasis, and survival of dermatofibrosarcoma protuberans (DFSP). OBJECTIVE: To measure DFSP incidence and assess metastasis and survival outcomes. METHODS: Incidence rate, overall and DFSP-specific survival outcomes for primary DFSP tumors contained in the Surveillance, Epidemiology, and End Results registry (SEER) were analyzed via quasi-poisson regression, Cox and competing risk analyses. RESULTS: DFSP incidence rate was 6.25 (95%CI, 5.93-6.57) cases per million person-years with significantly higher incidence observed among Black individuals than White individuals (8.74 vs 4.53). DSFP with larger tumor size (≥3 cm, Odds ratio,OR,2.24,95%CI,1.62-3.12,p <0.001) and tumors located on the head and neck (OR: 4.88, 95%CI, 3.31-7.18, p<0.001), and genitalia OR:3.16,95%CI:1.17-8.52, p value=0.023) were associated with significantly increased risk of metastasis whereas higher socioeconomic status (SES) was associated with significantly decreased risk of metastasis. Larger tumor size (≥3 cm), regardless of location, and age (≥60) were associated with significantly worse overall and cancer-specific survival. LIMITATIONS: Retrospective design of SEER CONCLUSION: DFSP incidence is 2-fold higher among Black than White individuals. The risk of DFSP metastasis is significantly increased with tumor size ≥3 cm and tumors located on head and neck, and genitalia. Larger tumor size (≥ 3cm), regardless of location, and age (≥60) are the most important prognostic indicators for survival.

Survival Analysis of Metastatic Early-Onset Colorectal Cancer Compared to Metastatic Average-Onset Colorectal Cancer: A SEER Database Analysis.

BACKGROUND: Early-onset colorectal cancer (EO-CRC) is defined as colorectal cancer diagnosed before the age of 50 years, and its incidence has been increasing over the last decade, now accounting for 10% of all new CRC diagnoses. Average-onset colorectal cancer (AO-CRC) has shown a steady decline in its incidence and related mortality over the past 20 years. The disparities in outcomes and overall survival (OS) between EO-CRC and AO-CRC are controversial. Our study compared OS and cause-specific survival (CSS) between metastatic EO-CRC (mEO-CRC) and metastatic AO-CRC (mAO-CRC) and identified the associated factors. METHODS: Data on patient characteristics, tumor characteristics, incidence, and mortality were obtained from the SEER database from 2010 to 2020. We identified 23,278 individuals aged > 18 years with a confirmed diagnosis of all histological subtypes of metastatic CRC (M1 on TNM stage) using ICD-O-3 site codes. mEO-CRC and mAO-CRC were compared. OS distributions and CCS were analyzed using the Kaplan-Meier method and log-rank test to assess differences. A Cox regression model was used to assess the associations between variables. RESULTS: mEO-CRC constituted 17.79% of the cases, whereas 82.21% had mAO-CRC. Most patients with mEO-CRC were 45-49 years old (47.66%), male (52.16%) and White (72.57%) and had adenocarcinoma histology (87.30%). Left colon tumors were most prevalent in both groups (40.26%) but were more prevalent in mEO-CRC patients than in mAO-CRC patients (49.63% vs. 38.23%, p < 0.001). Patients with mEO-CRC had higher OS (p < 0.001) and CSS (p < 0.001) than those with mAO-CRC. Patients with mEO-CRC also had significantly better median overall survival (30 months vs. 18 months, p < 0.001). The factors associated with worse OS included mAO-CRC (p < 0.001), mucinous adenocarcinoma (p < 0.001), male sex (p = 0.003), and a lack of surgical intervention (p < 0.001). CONCLUSIONS: Most patients with mEO-CRC fall within the range of 45 to 49 years of age. Patients with mEO-CRC were more likely to receive cancer-directed therapy (including chemotherapy and radiotherapy) and had better OS and CSS than those with mAO-CRC. This is likely attributable to the better performance status, fewer comorbidities, and better tolerance to cancer-directed therapy in mEO-CRC patients. The factors associated with worse OS and CSS were age > 50 years, mucinous adenocarcinoma, male sex, and no surgical treatment.

The impact of metformin on survival in diabetic endometrial cancer patients: a retrospective population-based analysis.

Purpose: The aim of our study was to assess overall survival and cancer-specific survival in endometrial cancer patients with type 2 diabetes mellitus (T2DM) using metformin. Methods: Patients with endometrial cancer and T2DM during 2000-2012 period were identified from the Lithuanian Cancer Registry and the National Health Insurance Fund database. Cancer-specific and overall survival were primary outcomes. Results: In our study we included 6287 women with endometrial cancer out of whom 664 were diagnosed with T2DM (598 metformin users and 66 never users). During follow-up (mean follow-up time was 8.97 years), no differences in risk of endometrial cancer specific mortality was observed in diabetic patients treated with metformin (Hazard Ratio (HR) 0.87, 95% Confidence Interval (CI) 0.70-1.07). Overall mortality in the diabetic metformin ever users' group was significantly higher compared with the non-diabetic endometrial cancer women (HR 1.17, 95% CI 1.03-1.32) and in the group of metformin never users with T2DM (HR 1.42, 95% CI 1.07-1.87). Conclusion: Our study results suggest no beneficial impact on overall and cancer-specific survival in endometrial cancer patients who were treated with metformin as part of their diabetes treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01358-3.

Triple-negative breast cancer survival prediction: population-based research using the SEER database and an external validation cohort.

Introduction: Triple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis. Methods: We included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis. Results: In the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately. Conclusion: These nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

Validation of the FIGO2023 staging system for early-stage endometrial cancer.

BACKGROUND: In 2023, the International Federation of Gynecology and Obstetrics (FIGO) updated the endometrial cancer staging system (FIGO2023). Our study aimed to validate the prognostic value of FIGO2023 in patients with early-stage EC (Stage I and Stage II). METHODS: After screening eligible EC patients from the Surveillance, Epidemiology and End Results (SEER) database, Kaplan-Meier cancer-specific survival (CSS) curves were used to evaluate the prognosis of patients with different stages. In addition, AUC, C-index, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Decision curve analysis (DCA) were used to comprehensively compare the efficacy of the new and the old staging system in predicting prognosis. RESULTS: A total of 33,156 patients were enrolled. The introduction of FIGO2023 significantly increased the proportion of stage II patients from 5.53 % to 24.76 %. The FIGO2023 defines different substages for patients, which show significant differences in CSS. Compared with FIGO2009, FIGO2023 performed better in discrimination, goodness of fit and clinical decision making. CONCLUSION: Compared with FIGO2009, FIGO2023 had a higher accuracy in predicting CSS in patients with early-stage EC in the SEER database.

Prognostic Factors Associated with Breast Cancer-Specific Survival from 1995 to 2022: A Systematic Review and Meta-Analysis of 1,386,663 Cases from 30 Countries.

Breast cancer is the fifth-ranked cancer globally. Despite early diagnosis and advances in treatment, breast cancer mortality is increasing. This meta-analysis aims to examine all possible prognostic factors that improve/deteriorate breast cancer-specific survival. MEDLINE, PubMed, ScienceDirect, Ovid, and Google Scholar were systematically searched until September 16, 2023. The retrieved studies from 1995 to 2022 accumulated 1,386,663 cases from 30 countries. A total of 13 out of 22 prognostic factors were significantly associated with breast cancer-specific survival. A random-effects model provided a pooled estimate of the top five poorest prognostic factors, including Stage 4 (HR = 12.12; 95% CI: 5.70, 25.76), followed by Stage 3 (HR = 3.42, 95% CI: 2.51, 4.67), a comorbidity index ≥ 3 (HR = 3.29; 95% CI: 4.52, 7.35), the poor differentiation of cancer cell histology (HR = 2.43; 95% CI: 1.79, 3.30), and undifferentiated cancer cell histology (HR = 2.24; 95% CI: 1.66, 3.01). Other survival-reducing factors include positive nodes, age, race, HER2-receptor positivity, and overweight/obesity. The top five best prognostic factors include different types of mastectomies and breast-conserving therapies (HR = 0.56; 95% CI: 0.44, 0.70), medullary histology (HR = 0.62; 95% CI: 0.53, 0.72), higher education (HR = 0.72; 95% CI: 0.68, 0.77), and a positive estrogen receptor status (HR = 0.78; 95% CI: 0.65, 0.94). Heterogeneity was observed in most studies. Data from developing countries are still scarce.

The impact of gender on survival outcomes in patients with small intestinal stromal tumors: a comprehensive analysis using the SEER database.

BACKGROUND: Small intestinal stromal tumors (SISTs) are a rare type of mesenchymal tumor. Gender is known to influence the incidence and prognosis of various tumors, but its role on the survival of SISTs at the population level remains unclear. Therefore, we aim to explore the relationship between gender and the prognosis of SISTs using the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: Data on SISTs patients from 2000 to 2019 were derived from the SEER database. Multiple imputation was used to address missing data. Kaplan-Meier analyses and Cox proportional hazard models were applied to evaluate the impact of demographic and clinical characteristics on overall survival (OS) and cancer-specific survival (CSS). RESULTS: A total of 3513 patients with SISTs were analyzed, including 1921 males and 1592 females. Kaplan-Meier analysis coupled with log-rank testing demonstrated a significantly higher mortality rate among male patients compared to females (P < 0.001). Notably, female patients exhibited superior OS (hazard ratio [HR] 0.808, 95% confidence interval [CI] 0.724-0.902, P < 0.001) and CSS (HR 0.801, 95% CI 0.692-0.927, P = 0.003) compared to male patients. While the mean 1-year CSS rates were comparable between genders (95.3% for males vs. 96.0% for females, P = 0.332), male patients consistently showed lower mean survival rates at 3-, 5-, and 10-year intervals. Surgical intervention significantly boosted 5-year OS and CSS rates in both male and female patients (P < 0.001). Multivariate Cox regression analysis identified age, sex, grade, TNM stage, surgery, and mitotic rate as independent risk factors for OS and CSS in patients with SISTs. CONCLUSIONS: Our findings suggest that male patients with SISTs have a higher risk of mortality compared to female patients, indicating that gender may serve as a predictive indicator for survival in this patient population.

A prognostic nomogram to predict the cancer-specific survival of patients with initially diagnosed metastatic gastric cancer: a validation study in a Chinese cohort.

BACKGROUND: Few studies have been designed to predict the survival of Chinese patients initially diagnosed with metastatic gastric cancer (mGC). Therefore, the objective of this study was to construct and validate a new nomogram model to predict cancer-specific survival (CSS) in Chinese patients. METHODS: We collected 328 patients with mGC from Northern Jiangsu People's Hospital as the training cohort and 60 patients from Xinyuan County People's Hospital as the external validation cohort. Multivariate Cox regression was used to identify risk factors, and a nomogram was created to predict CSS. The predictive performance of the nomogram was evaluated using the consistency index (C-index), the calibration curve, and the decision curve analysis (DCA) in the training cohort and the validation cohort. RESULTS: Multivariate Cox regression identified differentiation grade (P < 0.001), T-stage (P < 0.05), N-stage (P < 0.001), surgery (P < 0.05), and chemotherapy (P < 0.001) as independent predictors of CSS. Nomogram of chemotherapy regimens and cycles was also designed by us for the prediction of mGC. Thus, these factors are integrated into the nomogram model: the C-index value was 0.72 (95% CI 0.70-0.85) for the nomogram model and 0.82 (95% CI 0.79-0.89) and 0.73 (95% CI 0.70-0.86) for the internal and external validation cohorts, respectively. Calibration curves and DCA also demonstrated adequate fit and ideal net benefit in prediction and clinical applications. CONCLUSIONS: We established a practical nomogram to predict CSS in Chinese patients initially diagnosed with mGC. Nomograms can be used to individualize survival predictions and guide clinicians in making therapeutic decisions.

Practical Nomograms and Risk Stratification System for Predicting the Overall and Cancer-specific Survival in Patients with Anaplastic Astrocytoma.

OBJECTIVE: Anaplastic astrocytoma (AA) is an uncommon primary brain tumor with highly variable clinical outcomes. Our study aimed to develop practical tools for clinical decision-making in a population-based cohort study. METHODS: Data from 2997 patients diagnosed with AA between 2004 and 2015 were retrospectively extracted from the Surveillance, Epidemiology, and End Results database. The Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses were applied to select factors and establish prognostic nomograms. The discriminatory ability of these nomogram models was evaluated using the concordance index and receiver operating characteristic curve. Risk stratifications were established based on the nomograms. RESULTS: Selected 2997 AA patients were distributed into the training cohort (70%, 2097) and the validation cohort (30%, 900). Age, household income, tumor site, extension, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). In the training cohort, our nomograms for OS and CSS exhibited good predictive accuracy with concordance index values of 0.752 (95% CI: 0.741-0.764) and 0.753 (95% CI: 0.741-0.765), respectively. Calibration and decision curve analyses curves showed that the nomograms demonstrated considerable consistency and satisfactory clinical utilities. With the establishment of nomograms, we stratified AA patients into high- and low-risk groups, and constructed risk stratification systems for OS and CSS. CONCLUSIONS: We constructed two predictive nomograms and risk classification systems to effectively predict the OS and CSS rates in AA patients. These models were internally validated with considerable accuracy and reliability and might be helpful in future clinical practices.

Variations in germ cell tumor histology by age and implications for cancer-specific survival among pediatric and adult males: A population-based study.

PURPOSE: Few studies have quantified differences in histology and implications for survival between male children and adults with germ cell tumors (GCT). We evaluated these differences and associations with cancer-specific survival (CSS) using Surveillance, Epidemiology, and End Results (SEER) cancer registries. METHODS: SEER (1988-2016) was used to identify male patients 0 to 40 years of age diagnosed with seminoma and nonseminomatous GCT (NSGCT). Demographic and tumor characteristics were tabulated with histology distributions compared by age group (0-4, 12-18, 19-40 years old). CSS was evaluated in multivariable Cox proportional hazards regression models. RESULTS: Among 27,204 patients identified, 1,538 (5.7%) were pediatric (0-18 years). Seminoma (54.3%) predominated in adult patients (ages 19-40). Among 0 to 4 years-old, yolk sac tumor (71.2%) and teratoma (21.5%) were most common. Mixed GCT (52.7%) was most prevalent among 12 to 18 years-old with seminoma, embryonal, and teratoma occurring in 12 to 15% each. Relative to pediatric patients, adult patients had similar CSS for seminoma but worse CSS for NSGCT on Kaplan-Meier curves with 9 years mean follow-up. Choriocarcinoma and yolk sac tumors carried the worst prognosis relative to seminoma for both children (HR 5.7 and HR 11.1, respectively, both P < 0.01) and adults (HR 4.6 and HR 4.6, respectively, both P < 0.01) adjusted for stage. CONCLUSION: Histology of GCTs vary by age with yolk sac tumors and teratoma predominating for male patients 0 to 4 years, mixed GCT for 12 to 18 years, and seminoma for 19 to 40 years. Pediatric patients with NSGCT had higher CSS than their adult counterparts. Mixed GCT represented an increasing proportion of GCT over the study period. Age, stage, and histology impact CSS in both pediatric and adult populations.

Enhancing clinical decision-making: A novel nomogram for stratifying cancer-specific survival in middle-aged individuals with follicular thyroid carcinoma utilizing SEER data.

Background: Thyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. Methods: We collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. Results: A total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805-0.927), for the validation cohort it was 0.944 (95 % CI: 0.903-0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997-1.001). Calibration curves and AUC suggest that the model has good accuracy. Conclusions: We developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Development and validation of nomogram models for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: a cohort study based on the SEER database.

OBJECTIVE: To establish nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer liver metastasis (GCLM) patients. METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database for 5,451 GCLM patients diagnosed between 2010 and 2015 were analyzed. The cohort was divided into a training set (3,815 cases) and an internal validation set (1,636 cases). External validation included 193 patients from the Fourth Hospital of Hebei Medical University and 171 patients from the People's Hospital of Shijiazhuang City, spanning 2016-2018. Multivariable Cox regression analysis identified eight independent prognostic factors for OS and CSS in GCLM patients, including age, histological type, grade, tumor size, surgery, chemotherapy, bone metastasis, and lung metastasis. Two nomogram models were developed based on these factors and evaluated using time-dependent receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. RESULTS: Internal validation showed that the nomogram models outperformed the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system in predicting 1-year, 2-year, and 3-year OS and CSS in GCLM patients (1-year OS: 0.801 vs. 0.593, P < 0.001; 1-year CSS: 0.807 vs. 0.598, P < 0.001; 2-year OS: 0.803 vs. 0.630, P < 0.001; 2-year CSS: 0.802 vs. 0.633, P < 0.001; 3-year OS: 0.824 vs. 0.691, P < 0.001; 3-year CSS: 0.839 vs. 0.692, P < 0.001). CONCLUSION: This study developed and validated nomogram models using SEER database data to predict OS and CSS in GCLM patients. These models offer improved prognostic accuracy over traditional staging systems, aiding in clinical decision-making.