Dataset Information

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database.

ABSTRACT:

Objectives

This study performed a competing-risks analysis using data from the SEER database on penile cancer patients with the aim of identifying more accurate prognostic factors.

Methods

Data on patients with penile cancer were extracted from the SEER database. A univariate analysis used the cumulative incidence function and Gray's test, while multivariate analysis was performed using the Fine-Gray model. Cumulative hazards were compared with a competing-risks model constructed using Kaplan-Meier estimation.

Results

The multivariate Fine-Gray analysis indicated that being black (HR = 1.51, 95%CI: 1.10-2.07, P = .01), AJCC stage II (HR = 1.94, 95%CI: 1.36-2.77, P < .001), AJCC stage III (HR = 1.98, 95%CI: 1.34-2.91, P < .001), tumor size > 5 cm (HR = 2.23, 95%CI: 1.33-3.72, P < .05), and TNM stages N1 (HR = 2.49, 95%CI: 1.71-3.61, P < .001), N2 (HR = 3.25, 95%CI: 2.18-4.84, P < .001), N3 (HR = 5.05, 95%CI: 2.69-9.50, P < .001), and M1 (HR = 2.21, 95%CI: 1.28-3.84, P < .05) were statistically significant. The results obtained using multivariate Cox regression were different, while Kaplan-Meier curve analysis led to an overestimation of the cumulative risk of the patient.

Conclusions

This study established a competing-risks analysis model for the first time based on the SEER database for the risk assessment of penile cancer patients. The results may help clinicians to better understand penile cancer and provide these patients with more appropriate support.

SUBMITTER: Yang J

PROVIDER: S-EPMC6912058 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database.

Yang Jin J Pan Zhenyu Z He Yujing Y Zhao Fanfan F Feng Xiaojie X Liu Qingqing Q Lyu Jun J

Cancer medicine 20191027 18

<h4>Objectives</h4>This study performed a competing-risks analysis using data from the SEER database on penile cancer patients with the aim of identifying more accurate prognostic factors.<h4>Methods</h4>Data on patients with penile cancer were extracted from the SEER database. A univariate analysis used the cumulative incidence function and Gray's test, while multivariate analysis was performed using the Fine-Gray model. Cumulative hazards were compared with a competing-risks model constructed ...[more]

PMID: 31657120

Similar Datasets

Project description:Utilizing the traditional Cox regression model to identify the factors affecting the risk of mortality due to microinvasive cutaneous squamous cell carcinoma (micSCC) may produce skewed results. Since cause-specific mortality can guide clinical decision-making, this study employed the Fine-Gray model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify significant predictive variables for the risk of micSCC-related mortality. This study used the information of patients with micSCC who were listed in the SEER database during 2000-2015. Cox regression and Fine-Gray models were utilized for the multivariable analysis, and Gray's test and the cumulative incidence function were used for the univariable analyses. There were 100 patients who died from other reasons and 38 who died from micSCC among the 1259 qualified patients with micSCC. Most were female, white, married, had localized metastasis, etc. According to the univariable Gray's test (P < 0.05), the cumulative incidence rate for events of interest was strongly associated with age, sex, marital status, American Joint Committee on Cancer staging, radiation status, summary stage, chemotherapy status, surgery status, and tumor size. Multivariable Cox regression analysis and multivariable competing-risks analysis indicated that age, tumor size, and income were independent risk variables for the prognosis of patients with micSCC. In both age and tumor size variables, the competing-risks model showed a slight decrease in the hazard ratio and a slight narrowing of the 95% confidence interval compared with the Cox regression model. However, this pattern is not evident in the income variable. This study established a Fine-Gray model for identifying the independent risk factors that influence the risk of mortality among patients with micSCC. This study uncovers that, in the context of competing risks, age, tumor size, and income serve as independent risk factors influencing the risk of mortality due to micSCC among patients. Our findings have the potential to provide more accurate risk assessments for patient outcomes and contribute to the development of individualized treatment plans.

Project description:BackgroundHuman epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC) is a subtype of breast cancer with a worse prognosis. Little is known about the relationship between histology and prognosis among different distant metastasis sites (DMS). Our aims were to explore the prognostic value of histologic subtypes in different DMS and screen out specific subtypes with particular DMS that need more attention in HER2+ MBC.MethodsHER2+ MBC patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2014. Chi-squared tests were utilized to compare histologic subtypes in four DMS. The logistic regression analyses were used to control confounding factors. The log-rank tests were used to analyze the correlation of histologic subtype with disease-specific survival and overall survival. The survival data was analyzed using Kaplan-Meier methods.ResultsA total of 1174 HER2+ MBC patients were involved. First, the distribution of histological subtypes varied across metastatic sites, and the proportions of metastatic sites in different histological subtypes were also different. Furthermore, different histological subtypes within specific DMS showed divergent prognoses, and the different outcomes were shown by distinct DMS for specific histological subtypes. Among them, lobular carcinoma (ILC) subtypes showed the worst prognosis in bone metastasis, and lung metastasis predicted the worst prognosis in infiltration duct and lobular carcinoma (IDC-ILC) subtypes. After further consideration of hormone receptor (HR) status, the IDC-ILC subtype with liver metastasis in HR+/HER2+ MBC patients and the ILC subtype with bone metastasis in HR-/HER2+ MBC patients proved to be noteworthy.ConclusionsHistological subtypes are involved in determining the heterogeneity of HER2+ MBC patient prognosis, which is helpful to guide the prognosis prediction and monitoring of HER2+ breast cancer patients in clinics.

Project description:ObjectiveTo identify prognostic factors and establish nomograms for predicting overall survival (OS) and cause specific survival (CSS) of patients with non-metastatic chondrosarcoma.MethodsWe collected information on patients with non-metastatic chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database between 2005 and 2014, together with data from the First Affiliated Hospital of Zhengzhou University from 2011 to 2016. Variables including patients' baseline demographics (age, race, and gender), tumor characteristics (tumor size and extension, histology subtype, primary site, and American Joint Committee on Cancer [AJCC] stage), therapy (surgery, chemotherapy, and radiotherapy), and socioeconomic status (SES) were extracted for further analysis. OS and CSS were retrieved as our researching endpoints. Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. To validate nomograms internally and externally, we applied bootstrapped validation internally with the training dataset, while the dataset for external validation was obtained from the First Affiliated Hospital of Zhengzhou University. We estimated the discriminative ability of nomograms based on Cox proportional hazard regression models by means of calibration curves and the concordance index (C-index) of internal and external validation.ResultsAfter the implementation of exclusion criteria, there were 1267 patients in the training set and 72 patients in the testing set with non-metastatic chondrosarcomas. Age, gender, grade, histological subtype, primary site, surgery, radiation, chemotherapy, being employed/unemployed, tumor size, and tumor extension were significantly associated with prognosis in the univariate analysis. Age, gender, tumor size and extension, primary site, surgery, radiotherapy, chemotherapy, histological grade, and subtype were independent prognostic factors in the Cox models. The C-index of nomograms (internal: OS, 0.787; CSS, 0.821; external: OS, 0.777; CSS, 0.821) were higher than following conventional systems: AJCC sixth (OS, 0.640; CSS, 0.673) and seventh edition (OS, 0.675; CSS, 0.711).ConclusionsAge, gender, tumor size and extension, surgery, histological grade, and subtype were independent prognostic factors for both OS and CSS. In addition, we revealed that chondrosarcomas in the trunk, radiotherapy, and chemotherapy were correlated with poor prognosis. Our nomograms based on significant clinicopathologic features can well predict the 3-year and 5-year survival probability of patients with non-metastatic chondrosarcoma and assist oncologists in making accurate survival evaluation.

Dataset Information

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database.

Objectives

Methods

Results

Conclusions

Publications

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets