Surveillance, Epidemiology, And End Results Cohort Research Articles

Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer.

The study was outlined to develop and approve a nomogram and chance stratification demonstrate for foreseeing overall survival of Chinese patients with at first metastatic small-cell lung cancer (SCLC). We collected information from the Surveillance, Epidemiology, and End Results (SEER) database approximately Chinese SCLC patients with at first distant metastases between 2010 and 2015. Patients with incomplete data about the follow-up time or clinicopathological information were excluded. The included patients were randomized into the training and validation set. Univariate and multivariate Cox proportional hazard regression models were performed. By integrating the significant variables screened, a prescient nomogram and risk stratification model were developed. In addition, we collected 198 small-cell lung cancer patients with metastasis at diagnosis from the case database of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine as an external validation cohort. In all, 421 patients were screened from the SEER database. Multivariate examination showed that age (P = .049), sex (P = .001), grade (P = .008), chemotherapy (P = .001), liver metastasis (P = .001), and pleural invasion (P = .012) were independent prognostic factors. The C-indicator of the nomogram to anticipate overall survival was higher than that of the eighth edition of the American Joint Committee on Cancer Tumor Node Metastasis classification system (0.75 vs 0.543; P < .001). A risk stratification model was encouraged to be created to precisely classify patients into 2 prognostic bunches. The survival rates anticipated by the nomogram appeared to have critical qualifications from the Kaplan-Meier curves in the entire SEER cohort. Calibration curves and survival predictions also showed strong accuracy and consistency in the external validation cohort. The nomogram provided a clear prognostic superiority over the traditional Tumor Node Metastasis system. It could help clinicians make individual risk predictions for initially metastatic Chinese SCLC cancer patients and give necessary treatment recommendations.

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

Sex-Based Differences in Lung Cancer Incidence: A Retrospective Analysis of Two Large US-Based Cancer Databases

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

Epidemiology, prognostic factors, and survival analysis in small cell esophageal carcinoma: A population-based study with external validation.

Small cell esophageal carcinoma (SCEC) is a poorly differentiated esophageal neuroendocrine neoplasm with a poor prognosis. This study aimed to explore the factors and treatment approaches influencing the prognosis of SCEC. In this retrospective study, we collected data from the 18 Surveillance, Epidemiology, and End Results (SEER) registries cohort between 2004 and 2019, as well as from a Chinese institutional registry covering the period from 2012 to 2022. We assessed the annual percentage change (APC) in incidence of SCEC. Kaplan-Meier and Cox regression analyses were conducted to evaluate survival outcomes. Additionally, nomograms were developed for overall survival (OS) and cancer-specific survival (CSS) in the SEER cohort for SCEC and validated in an independent Chinese cohort. This analysis included 299 SCEC patients from the SEER cohort and 66 cases from the Chinese cohort. During the period of 2004-2019, the incidence of SCEC reached a plateau, with an APC of -1.40 (95% confidence interval [CI]: -4.3 to 1.40, P > 0.05). Multivariable Cox regression analysis revealed that age, distant metastasis, and chemotherapy were independent factors for OS, while distant metastasis and chemotherapy were independent factors for CSS. The nomograms developed for OS and CSS in SCEC exhibited remarkable accuracy and reliable predictive capacity in estimating 1-year, 3-year, and 5-year OS and CSS. SCEC is a rare malignancy with aggressive behavior. Distant metastasis is significantly associated with worse OS and CSS in patients with SCEC. Currently, chemotherapy remains the primary treatment approach for SCEC.

Constructing a Nomogram for Predicting Pelvic Osteosarcoma: A Retrospective Study Based on the SEER Database and a Chinese Cohort.

Aims/Background Generally, pelvic osteosarcoma has a worse prognosis compared with limb osteosarcoma. This study aims to create and validate a new nomogram for predicting the prognosis of pelvic osteosarcoma. Methods Clinical data of 62 patients derived from the Surveillance, Epidemiology, and End Results (SEER) database and 31 Chinese patients diagnosed with pelvic osteosarcoma were gathered. Kaplan-Meier survival analysis was utilized to calculate the median survival time for all variables. Univariate and multivariate Cox regression models were employed to identify the prognostic factors of pelvic osteosarcoma. A nomogram was constructed using data gleaned from the SEER cohort and verified using the receiver operating characteristic (ROC) curve and calibration plot in the Chinese cohort. Results Kaplan-Meier analysis revealed that individuals of other races (Asians) (hazard ratio (HR) = 0.24, 95% confidence interval (CI): 0.1-0.57, p = 0.001), aged ≤51 years old (HR = 0.4, 95% CI: 0.22-0.73, p = 0.003), and with tumor size ≤160 mm (HR = 0.37, 95% CI: 0.2-0.71, p = 0.03) had better survival outcomes. Conversely, factors such as no primary surgery (HR = 3.6, 95% CI: 1.81-7.15, p < 0.001), lung metastasis (HR = 1.96, 95% CI: 1.17-3.28, p = 0.010), and radiotherapy (HR = 1.89, 95% CI: 1.10-3.25, p = 0.021) were associated with poorer survival. Multivariate Cox analysis indicated that lung metastasis (HR = 2.57, 95% CI: 1.29-5.13, p = 0.008), other races (Asians) (HR = 0.23, 95% CI: 0.07-0.75, p = 0.015), tumor size (HR = 0.28, 95% CI: 0.13-0.62, p = 0.001) and age (HR = 0.3, 95% CI: 0.16-0.59, p < 0.001) were independent prognostic factors for pelvic osteosarcoma. Univariate and multivariate Cox regression models identified three independent variables in the training cohort: age, lung metastasis, and tumor size. A predictive nomogram was developed based on the data from the SEER cohort and validated in the Chinese cohort. The areas under the curves (AUCs) that are used to predict 1-year, 2-year, and 3-year survival rates were 0.81 (95% CI: 0.68-0.94), 0.75 (95% CI: 0.63-0.86), and 0.80 (95% CI: 0.70-0.89) in the training cohort, and 0.67 (95% CI: 0.30-1.04), 0.66 (95% CI: 0.43-0.90) and 0.71 (95% CI: 0.50-0.93) in the validation cohort. Conclusion The predictive nomogram constructed in this study facilitates accurate and effective prediction of the overall survival of patients with pelvic osteosarcoma and helps enhance the clinical decision-making process.

Socio-demographic determinants of myelofibrosis outcomes in an underserved center and the SEER national database

The influence of demographic characteristics and social determinants on cancer outcomes is widely recognized in various malignancies but remains understudied in myelofibrosis (MF). This study aims to investigate social and demographic variables associated with MF survival. We retrospectively reviewed data of biopsy-proven MF patients from the Surveillance, Epidemiology and End Results (SEER) database (2000–2021) and Montefiore Medical Center (2000–2023), an underserved inner-city hospital. The SEER cohort included 5,403 MF patients and was predominantly Non-Hispanic (NH) White (82%) with a median age of 69 years. The age-adjusted incidence rate of MF was 0.32 cases per 100,000 person-years, increasing annually by 1.3% from 2000 to 2021. Two- and five- year overall survival rates were 69% and 42%, respectively. Worse cause-specific survival was associated with older age, male sex, and diagnosis before 2011 (year of Ruxolitinib approval). NH-Black ethnicity, unmarried status and lower median income were independent predictors of worse overall survival. The single-center analysis included 84 cases, with a median age of 66 years. NH-White patients comprised 37% of the sample, followed by NH-Black (28.5%). Two- and five- year overall survival rates were 90% and 61%, respectively, with NH-Black patients exhibiting the lowest median survival, although the difference was not statistically significant. Age was a significant predictor of worse survival in this cohort. NH-Black and Hispanic patients lived in areas with higher socioeconomic and demographic stress compared to NH-White patients. Overall, this study highlights the association of social and demographic factors with MF survival and emphasizes the need for equitable healthcare and further exploration of social-demographic factors affecting MF survival.

Pediatric Axial Ewing Sarcoma: A Retrospective Population-Based Survival Analysis.

Ewing sarcomas of the axial skeleton represent a notable challenge for clinicians because of their aggressive presentation and tendency to obstruct neurovascular structures; however, little data exist regarding axial tumors in children. This study is the first population-based analysis assessing treatment regimens for axial Ewing sarcomas and their effects on cancer-specific survival and overall survival (OS). Data from 2004 to 2019 were collected for all patients aged 1 to 24 years from the Surveillance, Epidemiology, and End Results (SEER) database. Primary groups included pelvic tumors, thoracic tumors, and vertebral tumors. Chi-squared and Kaplan-Meier tests were used to assess associations between demographic variables, clinical and treatment characteristics, and patient survival. Pelvic tumors were most common, and 49.7% received chemotherapy/radiation. Vertebral tumors were least common, and 56.7% received chemotherapy/surgery/radiation. 53.5% of thoracic tumors received chemotherapy/surgery. Surgery was most common for thoracic tumors (80.2%) and rare for pelvic tumors (38.9%). Radiation therapy was most common for vertebral tumors (83.6%) and least common for thoracic tumors (36.0%). Pelvic tumors exhibited the lowest OS (1-year, 5-year, and 10-year OS: 96%, 70%, and 59%), followed by thoracic tumors (1-year, 5-year, and 10-year OS: 97%, 79%, and 66%) and vertebral tumors (1-year, 5-year, and 10-year OS: 92%, 77%, and 68%). This study underpins the importance of both early detection and chemotherapy-based multimodal therapy in the treatment of axial Ewing sarcoma in a pediatric population. A comparatively large decline in OS was observed between 5 and 10 years for patients with thoracic tumors, and this cohort's 10-year OS has not improved when compared with a similar SEER cohort from 1973 to 2011. Despite a growing body of research supporting definitive radiation therapy, a notable portion of patients with pelvic Ewing sarcoma did not receive radiation, representing an unmet need for this population.

Impacts of Tumor Stage at Diagnosis and Adjuvant Therapy on Long-Term Survival Outcomes in Patients With Triple-Negative Breast Cancer Achieving Pathologic Complete Response After Neoadjuvant Chemotherapy

BackgroundIt remains unknown whether the tumor stage at initial diagnosis and adjuvant treatments had any impacts on the long-term survival outcomes of patients with triple-negative breast cancer (TNBC) achieving pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT). MethodsClinical stage II-III patients with TNBC who achieved pCR after NACT were identified from the Surveillance, Epidemiology, and End Results (SEER) program (SEER cohort) and the National Clinical Research Center for Cancer (Tianjin) in China (TMUCIH cohort). Survival analyses were conducted based on tumor stages and the types of adjuvant treatment received by the patients. The outcomes of interest were overall survival (OS) and breast cancer-specific survival (BCSS). ResultsThe TMUCIH cohort comprised 178 patients with a median follow-up of 55.5 months. Two and 3 patients experienced BCSS and OS events, respectively. The SEER cohort included 1218 patients with a median follow-up of 65.5 months, where 53 and 78 patients experienced BCSS and OS events, respectively. Patients diagnosed with stage III disease had significantly higher hazards of death compared to stage II disease (OS: hazard ratio [HR], 3.34; 95% confidence interval [CI], 1.84-6.07; P < .001; BCSS: HR, 2.86; 95% CI, 1.38-5.92; P < .001). Adjuvant systemic and radiation therapy did not confer additional benefits to OS and BCSS. ConclusionTumor stage at initial diagnosis remains an independent predictor of long-term survival outcomes in patients with TNBC achieving pCR after NACT. Postoperative adjuvant chemotherapy and radiation therapy do not appear to provide additional benefit to their long-term prognosis.

Revolutionizing T3-4N0-2M0 gastric cancer staging with an innovative pathologic N classification system

BackgroundThe current pathologic N (pN) classification exhibits limitations in the prognostic stratification of patients with pT3-4N0-2M0 gastric cancer (GC). Therefore, this study aimed to develop and validate a new lymph nodal staging method based on the number of examined lymph nodes (ELNs) and lymph node ratio (LNR). MethodsData from 7883 patients with pT3-4N0-2M0 GC were collected from the Surveillance, Epidemiology, and End Results (SEER) database and Zhejiang Cancer Provincial Hospital. Optimal cutoff values for ELNs and LNR were determined using X-tile software. Kaplan-Meier methods, Log-rank tests, and Cox regression analyses were employed in this study. Patients were categorized into 3 new pN stages: new pN0 (pN0 with ELNs of >16), new pN1 (pN0 with ELNs of ≤16 or pN1-2 with LNR of ≤0.15), and new pN2 (pN1-2 with LNR of >0.15). The prognostic predictive power of both current and new pN staging was evaluated using the Akaike information criterion (AIC), Bayesian information criterion, concordance index (C-index), and receiver operating characteristic curve. ResultsThe new pN classification exhibited excellent performance in Kaplan-Meier survival analysis. After adjusting for confounding factors, the new pN staging emerged as an independent prognostic indicator in patients with GC. In the SEER cohort, the new pN staging demonstrated enhanced prognostic prediction accuracy over the American Joint Committee on Cancer pN staging (AIC: 75578.85 vs 75755.06; C-index: 0.642 vs 0.630; P < .001). Similar findings were validated in the Chinese cohort. ConclusionThis study developed and validated an improved pN classification for patients with pT3-4N0-2M0 GC. Surgeons should consider ELNs and LNR when assessing postoperative prognosis in patients with GC.

Development and validation of a nomogram integrating marital status for 5-year overall survival of chondrosarcoma: a population-based study.

The objective of this study was to evaluate the influence of marital status on overall survival (OS) and develop a nomogram for predicting 5-year OS in chondrosarcoma (CHS) patients. We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify CHS patients diagnosed between 2010 and 2018. Survival rates were calculated using Kaplan-Meier analysis. Prognostic factors were identified through univariate and multivariate analyses. An independent cohort was used for external validation of the nomogram. Performance evaluation of the nomogram was conducted using Harrell's concordance index (C-index), calibration plot, and decision curve analysis (DCA). In the SEER cohort, Kaplan-Meier analysis showed significant differences in OS among CHS patients with different marital statuses (P < 0.001), with widowed patients having the lowest OS. In terms of gender, there were significant survival differences based on marital status in females (P < 0.001), but not in males (P = 0.067). The OS of married and single females is significantly higher than that of married (P < 0.001) and single male (P = 0.006), respectively. Kaplan-Meier curves showed no significant difference in OS between groups stratified by either gender or marital status in the external cohort. Univariate and multivariate analyses confirmed that age at diagnosis, gender, marital status, tumor size, histological type, tumor grade, SEER stage, and surgery were independent prognostic factors for OS. The nomogram demonstrated high internal and external validation C-indexes of 0.818 and 0.88, respectively. Calibration plots, DCA curve, and Kaplan-Meier curve (P < 0.001) confirmed the excellent performance and clinical utility of the nomogram. Marital status was an independent factor influencing OS in CHS patients, with widowed patients having the worst prognosis. The OS of both married and single females is significantly higher than that of their male counterparts. However, these findings require further validation in a large independent cohort. While the contribution of marital status on predicting OS appears modest, our nomogram accurately predicted 5-year OS and identified high-risk groups, providing a valuable tool for clinical decision-making.

A population study comparing tracheal and lung adenoid cystic carcinoma.

Thoracic adenoid cystic carcinoma (ACC) is rare, and the differences between tracheal and lung lesions have not been fully understood. Patients were identified from a Chinese cancer center (FUSCC) (2005-2022) and the Surveillance, Epidemiology, and End Results (SEER) database (2000-2019). Incidence was calculated and trends were quantified. Clinicopathological features and overall survival (OS) were analyzed. Nomograms predicting OS were constructed. Totally, 55 tracheal adenoid cystic carcinoma (TACC) and 25 lung and bronchus adenoid cystic carcinoma (LACC) were included in a Chinese cohort, 121 TACC and 162 LACC included in the SEER cohort. There were larger tumor sizes, more lymph nodes and distant metastases for LACC than TACC patients. TACC patients are more likely to get local treatments. Patients with LACC had significantly worse median OS than patients with TACC (SEER cohort: 68.0 months vs. 109.0 months, p = 0.001, Chinese cohort: 62.9 months vs. 124.8 months, p = 0.061). Age, lymph node metastasis, distant metastasis and local treatment were identified as independent prognostic factors for OS of TACC. Distant metastasis and local treatment were identified for LACC. Specifically, surgery alone or in combination with radiotherapy is crucial for improving survival in both TACC and LACC. Only TACC benefits from radiotherapy alone, while chemotherapy does not improve survival for either. The nomograms constructed using these factors revealed good prognostic accuracy. LACC is more aggressive and has a worse prognosis than TACC. TACC patients have more opportunities for local treatment, which is important for the prognosis of both TACC and LACC. Nomograms were created for TACC and LACC to aid in personalized survival predictions and clinical decisions.

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma

PurposeMachine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremity LMS. MethodsThe Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologic extremity LMS (n = 634). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of extremity LMS patients (n = 46). ResultsAll ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.75–0.76 at the 5-year time point. The Random Forest (RF) model was the best performing model and used for external validation. This model also performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.90 and 0.87, respectively. The RF model was well calibrated on external validation. This model has been made publicly available at https://rachar.shinyapps.io/lms_app/ ConclusionsML models had excellent performance for survival prediction of extremity LMS. Future studies incorporating a larger institutional cohort may be needed to further validate the ML model for LMS prognostication.

Glioblastoma patients' survival and its relevant risk factors during the pre-COVID-19 and post-COVID-19 pandemic: real-world cohort study in the USA and China.

Although the COVID-19 pandemic has exerted potential impact on patients with glioblastomas (GBMs), it remains unclear whether the survival and its related risk factors of GBM patients would be altered or not during the period spanning from pre-COVID-19 to post-COVID-19 pandemic era. This study aimed to clarify the important issues above. Two observational cohorts were utilized, including the nationwide American cohort from the Surveillance, Epidemiology, and End-Results (SEER) and the Chinese glioblastoma cohort (CGC) at our institution during 2018-2020. Demographics, tumour features, treatment regimens and clinical outcomes were collected. Cox regression model, competing risk model, and subgroup and sensitivity analysis were used to dynamically estimate the survival and its relevant risk factors over different diagnosis years from the pre-COVID-19 (2018 and 2019) to post-COVID-19 (2020) pandemic. Causal mediation analysis was further adopted to explore the potential relationship between risk factors and mortality. This study included 11321 GBM cases in SEER and 226 GBM patients in CGC, respectively. Instead of the diagnostic years of 2018-2020, the prognostic risk factors, such as advanced age, bilateral tumour and absence of comprehensive therapy (surgery combined with chemoradiotherapy), were identified to persistently affect GBM survival independently during the period from 2018 to 2020 in the SEER cohort (all P < 0.05). In CGC, lack of comprehensive therapy for GBM patients were restated as survival risk factors during the same timeframe. Causal mediation analysis showed that the effect of comprehensive therapy on all-cause mortality played a determinant role (direct effect value -0.227, 95% CI -0.248 to -0.207), which was partially mediated by age (9.11%) rather than tumour laterality. As the timeframe shifted from pre-COVID-19 to post-COVID-19 pandemic, survival of GBM patients remained stable, yet advanced age, bilateral tumours, and passive treatment continuingly impacted GBM survival. It is necessary to optimize the comprehensive treatment for GBM patients even in the post-pandemic era.

Establishment of Prognostic Nomogram for Male Breast Cancer Patients: A Surveillance, Epidemiology and End Results Database Analysis.

Male breast cancer (MBC) represents a rare subtype of breast cancer, with limited prognostic factor studies available. The purpose of this research was to develop a unique nomogram for predicting MBC patient overall survival (OS) and breast cancer-specific survival (BCSS). From 2010 to 2020, clinical characteristics of male breast cancer patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Following univariate and multivariate analyses, nomograms for OS and BCSS were created. Kaplan-Meier plots were further generated to illustrate the relationship between independent risk variables and survival. The nomogram's ability to discriminate was measured by employing the area under a time-dependent receiver operating characteristic curve (AUC) and calibration curves. Additionally, when the nomogram was used to direct clinical practice, we also used decision curve analysis (DCA) to evaluate the clinical usefulness and net clinical benefits. A total of 2143 patients were included in this research. Univariate and multivariate analysis showed that age, grade, surgery, chemotherapy status, brain metastasis status, subtype, marital status, race, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with OS. Lung metastasis, age, marital status, grade, surgery, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with BCSS. By comprising these variables, a predictive nomogram was constructed in the SEER cohort. Then, it could be validated well in the validation cohort by receiver operating characteristics (ROCs) curve and calibration plot. Furthermore, the nomogram demonstrated better decision curve analysis (DCA) results, indicating the ability to forecast survival probability with greater accuracy. We created and validated a unique nomogram that can assist clinicians in identifying MBC patients at high risk and forecasting their OS/BCSS.

The 31-Gene Expression Profile Test Outperforms AJCC in Stratifying Risk of Recurrence in Patients with Stage I Cutaneous Melanoma.

Patients with stage I cutaneous melanoma (CM) are considered at low risk for metastasis or melanoma specific death; however, because the majority of patients are diagnosed with stage I disease, they represent the largest number of melanoma deaths annually. The 31-gene expression profile (31-GEP) test has been prospectively validated to provide prognostic information independent of staging, classifying patients as low (Class 1A), intermediate (Class 1B/2A), or high (Class 2B) risk of poor outcomes. Patients enrolled in previous studies of the 31-GEP were combined and evaluated for recurrence-free (RFS) and melanoma-specific survival (MSS) (n = 1261, "combined"). A second large, unselected real-world cohort (n = 5651) comprising clinically tested patients diagnosed 2013-2018 who were linked to outcomes data from the NCI Surveillance, Epidemiology, and End Results (SEER) Program registries was evaluated for MSS. Combined cohort Class 1A patients had significantly higher RFS than Class 1B/2A or Class 2B patients (97.3%, 88.6%, 77.3%, p < 0.001)-better risk stratification than AJCC8 stage IA (97.5%) versus IB (89.3%). The SEER cohort showed better MSS stratification by the 31-GEP (Class 1A = 98.0%, Class 1B/2A = 97.5%, Class 2B = 92.3%; p < 0.001) than by AJCC8 staging (stage IA = 97.6%, stage IB = 97.9%; p < 0.001). The 31-GEP test significantly improved patient risk stratification, independent of AJCC8 staging in patients with stage I CM. The 31-GEP provided greater separation between high- (Class 2B) and low-risk (Class 1A) groups than seen between AJCC stage IA and IB. These data support integrating the 31-GEP into clinical decision making for more risk-aligned management plans.

Optimal treatment strategy and prognostic analysis for patients with non-metastatic pT4 colon adenocarcinoma.

This study endeavored to explore the optimal treatment strategy and conduct a prognostic analysis for patients diagnosed with pT4M0 (pathologic stage T4) colon adenocarcinoma (COAD). A total of 8,843 patients diagnosed with pT4M0 COAD between January 2010 and December 2015 were included in this study from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training set and an internal validation set using a 7:3 ratio. Variables that demonstrated statistical significance (P<0.05) in univariate COX regression analysis or held clinical significance were incorporated into the multivariate COX regression model. Subsequently, this model was utilized to formulate a nomogram. The predictive accuracy and discriminability of the nomogram were assessed using the C-index, area under the curve (AUC), and calibration curves. Decision curve analysis (DCA) was conducted to confirm the clinical validity of the model. In the entire SEER cohort, the 3-year overall survival (OS) rate (74.22% vs. 63.20%, P<0.001) and the 3-year cancer-specific survival (CSS) rate (76.25% vs. 66.98%, P<0.001) in the surgery combined with postoperative adjuvant therapy (S+ADT) group surpassed those in the surgery (S) group. Multivariate COX regression analysis of the training set unveiled correlations between age, race, N stage, serum CEA (carcinoembryonic antigen), differentiation, number of resected lymph nodes, and treatment modalities with OS and CSS. Nomograms for OS and CSS were meticulously crafted based on these variables, achieving C-indexes of 0.692 and 0.690 in the training set, respectively. The robust predictive ability of the nomogram was further affirmed through receiver operating characteristic (ROC) and calibration curves in both the training and validation sets. In individuals diagnosed with pT4M0 COAD, the integration of surgery with adjuvant chemoradiotherapy demonstrated a substantial extension of long-term survival. The nomogram, which incorporated key factors such as age, race, differentiation, N stage, serum CEA level, tumor size, and the number of resected lymph nodes, stood as a dependable tool for predicting OS and CSS rates. This predictive model held promise in aiding clinicians by identifying high-risk patients and facilitating the development of personalized treatment plans.

Nomogram for predicting occurrence and prognosis of liver metastasis in elderly colorectal cancer patients: a population-based study.

This study aimed to explore independent risk and prognostic factors in elderly patients with colorectal cancer liver metastasis (ECRLM) and generate nomograms for predicting the occurrence and overall survival (OS) rates of such patients. Elderly colorectal cancer patients (ECRC) from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. External validation relied on Chinese patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were employed to identify liver metastasis (LM) risk variables, which were used to create a nomogram to estimate LM probabilities in patients with ECRC. Univariate and multivariable Cox analyses were performed to identify prognostic variables and further derive nomograms that could predict the OS of patients with ERCLM. Differences in lifespan were assessed using the Kaplan-Meier analysis. Finally, the quality of the nomograms was verified using decision curve analysis (DCA), calibration curves, and receiver operating characteristic curves (ROC). In the SEER cohort, 32,330 patients were selected, of those, 3,012 (9.32%) were diagnosed with LM. A total of 188 ECRLM cases from a Chinese medical center were assigned for external validation. LM occurrence can be affected by 13 factors, including age at diagnosis, marital status, race, bone metastases, lung metastases, CEA level, tumor size, Grade, histology, primary site, T stage, N stage and sex. Furthermore, in ECRLM patients, 10 variables, including age at diagnosis, CEA level, tumor size, lung metastasis, bone metastasis, chemotherapy, surgery, N stage, grade, and race, have been shown to be independent prognostic predictors. The results from both internal and external validation revealed a high level of accuracy in predicting outcomes, as well as significant clinical utility, for the two nomograms. We created two nomograms to predict the occurrence and prognosis of LM in patients with ECRC, which would contribute significantly to the improvement in disease detection accuracy and the formulation of personalized cures for that particular demographic.

Differences in Survival between Black and White Patients with Head and Neck Squamous Cell Carcinoma: Comparison of Data from the DOD Central Cancer Registry and SEER.

Barriers to health care access may contribute to the poorer survival of Black patients with head and neck squamous cell carcinoma (HNSCC) than their White counterparts in the U.S. general population. The Department of Defense's (DOD) Military Health System (MHS) provides universal health care access to all beneficiaries with various racial backgrounds. We compared overall survival of patients with HNSCC by race in the MHS and the general population, respectively, to assess whether there were differences in racial disparity between the two populations. The MHS patients were identified from the DOD's Central Cancer Registry (CCR) and the patients from the U.S. general population were identified from the NCI's Surveillance, Epidemiology and End Results (SEER) program. For each cohort, a retrospective study was conducted comparing survival by race. Black and White patients in the CCR cohort had similar survival in multivariable Cox regression models with a HR of 1.04 and 95% confidence interval (95% CI) of 0.81 to 1.33 after adjustment for the potential confounders. In contrast, Black patients in the SEER cohort exhibited significantly worse survival than White patients with an adjusted HR of 1.47 (95% CI = 1.43-1.51). These results remained similar in the subgroup analyses for oropharyngeal and non-oropharyngeal sites, respectively. There was no racial difference in survival among patients with HNSCC in the MHS system, while Black patients had significantly poorer survival than White patients in the general population. Equal access to health care could reduce racial disparity in overall survival among patients with HNSCC.

Development and validation of a predictive model associated with lymph node metastasis of gastric signet ring carcinoma patients.

The risk factors for lymph node metastasis (LNM) in patients with gastric signet ring cell carcinoma (GSRC) have not been well-defined. This study was designed to prognosticate LNM in patients with GSRC by constructing and verifying a nomogram. A total of 2789 patients with GSRC from the Surveillance, Epidemiology, and End Results (SEER) database and Yijishan Hospital of Wannan Medical College (YJS) were retrospectively reviewed. A predictive model was established using logistic regression based on the SEER cohort. The performance of the model was evaluated using the concordance index (C-index) and decision curve analysis (DCA). In addition, its robustness was validated using the YJS cohort. Four independent predictors of LNM were identified in the SEER cohort. Next, a nomogram was constructed by incorporating these predictors. The C-index were 0.800 (95% confidence interval [CI] = 0.781-0.819) and 0.837 (95% CI = 0.784-0.890) in the training and external validation cohorts, respectively. The outcomes of DCA supported good clinical benefits. The proposed model for evaluating the LNM in patients with GSRC can help to avoid the misdiagnosis risk of N-stage, assist to screen the population suitable for neoadjuvant therapy and help clinicians to optimize clinical decisions.

Establishment and validation of a nomogram to select patients with metastatic sarcomatoid renal cell carcinoma suitable for cytoreductive radical nephrectomy

IntroductionMetastatic renal cell carcinoma (mRCC) with sarcomatoid features has a poor prognosis. Cytoreductive radical nephrectomy (CRN) can improve prognosis, but patient selection is unclear. This study aimed to develop a prediction model for selecting patients suitable for CRN.Materials and methodsPatients with a diagnosis of mRCC with sarcomatoid features in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively reviewed. CRN benefit was defined as a survival time longer than the median overall survival (OS) in patients who did not receive CRN. A prediction nomogram was established and validated using the SEER cohort (training and internal validation) and an external validation cohort.ResultsOf 900 patients with sarcomatoid mRCC, 608 (67.6%) underwent CRN. OS was longer in the CRN group than in the non-CRN group (8 vs. 6 months, hazard ratio (HR) = 0.767, p = 0.0085). In the matched CRN group, 124 (57.7%) patients survived &amp;gt;6 months after the surgery and were considered to benefit from CRN. Age, T-stage, systematic therapy, metastatic site, and lymph nodes were identified as independent factors influencing OS after CRN, which were included in the prediction nomogram. The monogram performed well on the training set (area under the receiver operating characteristic (AUC) curve = 0.766, 95% confidence interval (CI): 0.687–0.845), internal validation set (AUC = 0.796, 95% CI: 0.684–0.908), and external validation set (AUC = 0.911, 95% CI: 0.831–0.991).ConclusionsA nomogram was constructed and validated with good accuracy for selecting patients with sarcomatoid mRCC suitable for CRN.