A Competing Risk Analysis Study of Prognosis in Patients with Esophageal Carcinoma 2006-2015 Using Data from the Surveillance, Epidemiology, and End Results (SEER) Database

Impact of marital status on overall survival in patients with early-stage hepatocellular carcinoma

The purpose of the present research was to assess the prognostic impact of marital status in hepatocellular carcinoma (HCC) patients with tumors ≤ 2 cm (stage Ia) based on the data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients who received a histopathologic HCC diagnosis between 2004 and 2016 were recruited. Overall survival (OS) was the major outcome measure. The Cox regression model and the Fine-Gray regression model were used for the purpose of comparing and examining the prognostic value of marital status for OS. The data for a total of 2446 stage Ia HCC patients were extracted from the database. The median overall survival time was 96.0 months, with 5-year and 10-year overall survival rates of 58.2% and 45.8%, respectively. In both the Fine-Gray regression model and Cox regression model, marital status [married vs. unmarried and others, both P < 0.001, hazard ratio (HR) = 1.389 for Cox and HR = 1.378 for Fine-Gray], age at diagnosis, tumor grade, and surgery at the primary site independently served as prognostic indicators associated with OS. In conclusion, positive marital status was independently associated with better OS for stage Ia HCC patients, and its prognostic influence should be validated in the near future.

Cardiovascular disease related death among patients with esophagus cancer: A population-based competing risk analysis

The proportion of non-cancer death in patients with esophagus cancer (EC) still increasing, especially cardiovascular disease (CVD) related death. The aim of this study was assess non-cancer causes of death and identified independent risk factors of CVD related death in EC patients. Patients diagnosed with EC were extracted from the Surveillance, Epidemiology, and End Result database (SEER) database for analysis. Standardized mortality rates (SMRs) for non-EC deaths were calculated, the risk of death were assessed and compared with US general population. Multivariate competitive risk analysis were performed to select independent risk factors for death from CVD in EC patients. A total of 43739 EC patients were enrolled and 35139 died during follow-up, of which 4248 died from non-cancer cause of death. The risk of non-cancer death in EC patients was 2.27-fold higher than in the general population (SMR=2.27; 95% CI, 2.20-2.34). CVD were the most important cause of non-cancer death in EC patients, accounting for 43.4% of non-cancer of deaths. Compare with the general population, EC patients have higher risk of death from disease of heart (SMR, 2.24; 95% CI, 2.13-2.35), pneumonia and influenza (SMR, 2.92; 95% CI, 2.50-3.39), septicemia (SMR, 5.01; 95% CI, 4.30-5.79), along with other causes. Patients with advanced age and patients who received radiotherapy has higher risk of death caused by CVD, patients with female sex, poor differentiated and undifferentiated, regional and distant stage, married, diagnosed between 2010-2016 has lower risk of CVD related death, compared with patients without any treatment measures, patients received chemotherapy alone has lower risk of death from CVD. Non-cancer cause of death has become an important cause of death in EC patients. Improving public awareness of the major risk factors for non-cancer death is beneficial to the prevention and treatment of malignant tumors.

Competing risk of the specific mortality among Asian-American patients with prostate cancer: a surveillance, epidemiology, and end results analysis

Background

Adopted the competing-risk model to investigate the relevant factors affecting the prostate cancer (PCa)-specific mortality among Asian-American PCa patients based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

The information of 26,293 Asian-American patients diagnosed with PCa between 2004 and 2015 were extracted from the SEER 18 database. Subjects were divided into three groups: died of PCa, died of other causes, survival based on the outcomes at the end of 155 months’ follow-up. Multivariate analysis was performed by the Fine-gray proportional model. Meanwhile, subgroup analyses were conducted risk stratification by race and age.

Results

Age ≥ 65 years [Hazard ratio (HR) = 1.509, 95% confidence interval (CI) 1.299–1.754], race (HR = 1.220, 95% CI 1.028–1.448), marital status (unmarried, single or widowed, HR = 1.264, 95% CI 1.098–1.454), tumor grade II (HR = 3.520, 95% CI 2.915–4.250), the American Joint Committee on Cancer (AJCC) stage (T3: HR = 1.597, 95% CI 1.286–1.984; T4: HR = 2.446, 95% CI 1.796–3.331; N1: HR = 1.504, 95% CI 1.176–1.924; M1: HR = 9.875, 95% CI 8.204–11.887) at diagnosis, radiotherapy (HR = 1.892, 95% CI 1.365–2.623), regional nodes positive (HR = 2.498, 95% CI 1.906–3.274) increased risk of PCa-specific mortality for Asian-American PCa patients, while surgical (HR = 0.716, 95% CI 0.586–0.874) reduced the risk.

Conclusion

The study findings showed that age, race, marital status, tumor grade (II), AJCC stages (T3, T4, N1, M1) at diagnosis, radiotherapy, regional nodes positive and surgery was associated with the specific mortality of PCa patients among Asian-Americans.

Ideal Anatomical Landmark Points for Thoracic Esophagus Segmentation in the Chinese Population

Introduction: The standards of esophagus segmentation remain different between the Japan Esophageal Society (JES) guideline and the Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) guideline. This study aimed to present variations in the location of intrathoracic esophageal adjacent anatomical landmarks (EAALs) and determine an appropriate method for segmenting the thoracic esophagus based on the relatively fixed EAALs. Patients and Methods: The distances from the upper incisors to the upper border of the esophageal hiatus, lower border of the inferior pulmonary vein (LPV), tracheal bifurcation, lower border of the azygous vein (LAV), and thoracic inlet were measured in the patients undergoing thoracic surgery. The median distances between the EAALs and the specified starting points, as well as reference value ranges and ratios, were obtained. The variation coefficients of distances and ratios from certain starting points to different EAALs were calculated and compared to determine the relatively fixed landmarks. Results: This study included 305 patients. The average distance from the upper incisors to the upper border of the cardia, the midpoint between the tracheal bifurcation and esophageal hiatus (MTBEH), LPV, LAV, tracheal bifurcation, and thoracic inlet were 41.6, 35.3, 34.8, 29.4, 29.5, and 20.3 cm, respectively. The distances from the upper incisors or thoracic inlet to any intrathoracic EAALs in men were higher than in women. In addition, the height, weight, and body mass index (BMI) were correlated with the distances. The ratio of the distance between the upper incisors and tracheal bifurcation to the distance between the upper incisors and upper border of the cardia and the ratio of the distance between the thoracic inlet and tracheal bifurcation to the distance between the thoracic inlet and upper border of the cardia possessed relatively smaller coefficients of variation. Conclusion: The distances from the EAALs to the upper incisors vary with height, weight, BMI, and gender. Compared with distance, the ratios are more suitable for esophagus segmentation. Tracheal bifurcation and MTBEH are ideal EAALs for thoracic esophagus segmentation, and this is consistent with the JES guideline recommendation.

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Clinical Characteristics, Prognosis, and Nomogram for Esophageal Cancer Based on Adenosquamous Carcinoma: A SEER Database Analysis

Objective: Esophageal adenosquamous carcinoma (ASC) is a rare pathological type of cancer. Its clinical features and prognosis is poorly understood. The purpose of this study was to identify the characteristics of ASC patients and analyze the risk factors of esophageal carcinoma.

Methods: Patients with esophageal cancer in the SEER database diagnosed from 1975–2016 were obtained. The epidemiology, clinical characteristics, and outcomes between these three groups were compared. The nomogram and online dynamic nomogram were constructed according to the Cox proportional hazard model.

Results: The age-adjusted incidences of AC (1975–1999), AC (1999–2016), and ASC (1975–1989) increased over time (p < 0.05). Age-adjusted incidences of SqCC (1986–2012) and ASC (1989–2016) decreased (p < 0.05). Survival of patients with ASC was significantly worse when compared to AC and SqCC (ASC vs. AC, p < 0.001, ASC vs. SqCC, p = 0.01). ASC, older age, black race, male, overlapping site, higher tumor grade, lymph node metastasis, and a higher summary stage or AJCC stage were considered to be risk factors for a poor survival in the multivariate Cox analysis. The ROC curves and AUC indicated that the model has a good discrimination ability (AUC were 0.774 for a 3-year OS and 0.782 for a 5-year OS). An online dynamic nomogram was built based on the Cox proportional hazard model for convenient clinical use.

Conclusions: ASC is somewhat closer to AC rather than SqCC in terms of the demographics and tumor site, but has a worse OS than both AC and SqCC.

Nomograms for predicting survival in early-onset esophageal cancer

Background: This study aimed to develop nomograms predicting the overall survival (OS) of patients younger than 50 years old with esophageal cancer.Methods: We selected patients included 2004-2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed using significant variables from multivariable Cox analyses. The discrimination and calibration power of the models were evaluated using concordance indexes (C-indexes) and calibration curves. Decision curve analysis was used to assess the clinical net benefits of the nomograms.Results: Of 1,997 selected patients, 53.2% had advanced-stage tumor. Race, grade, T stage, N stage, and treatment were independent factors affecting OS in early-stage patients. The C-indexes of the corresponding nomogram were 0.710 (95% CI = 0.684-0.736) and 0.681 (95% CI = 0.640-0.722) in training and validation sets, respectively. Grade, marital status, and treatment were independent factors affecting OS in advanced-stage patients. The C-indexes of the corresponding nomogram were 0.677 (95% CI = 0.653-0.701) and 0.675 (95% CI = 0.638-0.712) in training and validation sets, respectively. Calibration curves demonstrated high consistency between predicted and actual survival.Conclusion: We constructed and verified nomograms that could accurately predict the survival rate of esophageal cancer in patients younger than 50 years old. This may help clinicians better understand prognostic factors.