Competing risk analysis of primary tracheal carcinoma based on SEER database

Prognostic factors and constructing a nomogram in tracheal cancer patients treated with surgical intervention: A study based on SEER database

Although surgery is considered the first choice of treatment for patients diagnosed with tracheal cancer, the prediction of overall survival (OS) in patients undergoing surgical intervention is poor. To address this issue, we developed a nomogram that combined a risk classification system to estimate the OS of patients with tracheal cancer who underwent surgical intervention. A total of 525 qualified patients were selected from the surveillance, epidemiology, and end results database between 1975 and 2018 and were randomly divided into training and validation cohorts (7:3). The parameters of independent prognostic ability were determined using Cox regression analysis, and a nomogram was formed. The predictive ability of the nomogram was tested using the area under the curve of receiver operating characteristic curves and calibration curves. Survival curves were assessed between the different risk classification groups using the Kaplan-Meier method. The results indicated that Age, stage, histology, and tumor size were independent prognostic factors and were included in the predictive model. The calibration plots demonstrated good agreement between the nomogram prediction and actual observation for 24- and 36-month OS. The receiver operating characteristic curves suggested that the predictive model had good discrimination ability, with the area under the curves (training group 0.817, 0.785, and 0.801, respectively) and validation group (0.744, 0.794, and 0.822, respectively). Furthermore, the low-risk group had a better prognosis than the high-risk group in the total, training, and validation cohorts (all P < .001). This study established a novel nomogram system to predict OS and identify independent prognostic factors in patients with tracheal cancer who underwent surgical intervention. This model has the potential to assist doctors in making decisions regarding treatment options.

Predictors of Lymph Node Metastasis in Siewert Type II T1 Adenocarcinoma of the Esophagogastric Junction: A Population-Based Study

Background:

Endoscopic resection has been introduced as an alternative treatment for superficial adenocarcinoma of the esophagogastric junction (AEG), but is limited by positive nodal status. We aimed to investigate the predictors of lymph node metastasis (LNM) in patients with Siewert type II T1 AEG.

Methods:

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify eligible patients with Siewert type II T1 AEG. The prevalence of LNM was assessed. Logistic regression analysis with multivariable adjustment was used to determine predictors of LNM. We also performed Cox regression analysis to examine the prognostic value of LNM, which was further confirmed by competing risk analysis and cumulative incidence function (CIF).

Results:

In total, 2651 patients with T1 AEG were included, with a median age of 69 years and a median follow-up of 28 months. The overall prevalence of LNM was 17.2% in T1 AEG. When stratified by tumor invasion depth, the prevalence of LNM was 8.5% for intramucosal tumors and 22.6% for submucosal tumors. Adjusted logistic regression analysis showed that age, sex, tumor grade, tumor size and tumor infiltration depth were independent predictors of LNM in T1 AEG. Multivariate Cox regression analysis revealed that positive nodal status was significantly associated with worse overall survival and cancer-specific survival (CSS). Subgroup analysis consistently demonstrated that patients with LNM had significantly poorer CSS than those without LNM in most subgroups. Finally, the CIF was calculated, showing that patients with LNM had a significantly higher cancer-specific death rate than those without LNM.

Conclusions:

This population-based study identified age, sex, tumor grade, tumor infiltration depth and tumor size as independent predictors of LNM in T1 AEG. Considering the high prevalence of LNM in T1 AEG, endoscopic resection for curative aims may only be introduced in patients without high risks of LNM.

Nomograms for Estimating Cause-Specific Death Rates of Patients With Inflammatory Breast Cancer: A Competing-Risks Analysis

Purpose:

Inflammatory breast cancer (IBC) is a rare, aggressive and special subtype of primary breast cancer. We aimed to establish competing-risks nomograms to predict the IBC-specific death (BCSD) and other-cause-specific death (OCSD) of IBC patients.

Methods:

We extracted data on primary IBC patients from the SEER (Surveillance, Epidemiology, and End Results) database by applying specific inclusion and exclusion criteria. Cumulative incidence function (CIF) was used to calculate the cumulative incidence rates and Gray’s test was used to evaluate the difference between groups. Fine-Gray proportional subdistribution hazard method was applied to identify the independent predictors. We then established nomograms to predict the 1-, 3-, and 5-year cumulative incidence rates of BCSD and OCSD based on the results. The calibration curves and concordance index (C-index) were adopted to validate the nomograms.

Results:

We enrolled 1699 eligible IBC patients eventually. In general, the 1-, 3-, and 5-years cumulative incidence rates of BCSD were 15.3%, 41.0%, and 50.7%, respectively, while those of OCSD were 3.0%, 5.1%, and 7.4%. The following 9 variables were independent predictive factors for BCSD: race, lymph node ratio (LNR), AJCC M stage, histological grade, ER (estrogen receptor) status, PR (progesterone receptor) status, HER-2 (human epidermal growth factor-like receptor 2) status, surgery status, and radiotherapy status. Meanwhile, age, ER, PR and chemotherapy status could predict OCSD independently. These factors were integrated for the construction of the competing-risks nomograms. The results of calibration curves and C-indexes indicated the nomograms had good performance.

Conclusions:

Based on the SEER database, we established the first competing-risks nomograms to predict BCSD and OCSD of IBC patients. The good performance indicated that they could be incorporated in clinical practice to provide references for clinicians to make individualized treatment strategies.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis

INTRODUCTION

Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.

METHODS

Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.

RESULTS

We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.