A comprehensive prognostic analysis of cause-specific mortality in patients with ovarian serous cystadenocarcinoma using a competing-risks model: a case study of the SEER database

OBJECTIVE: This retrospective study employed a competing-risks analysis utilizing the Surveillance, Epidemiology, and End Results (SEER) database to identify precise prognostic factors associated with ovarian serous cystadenocarcinoma (OSCC) in patients. PATIENTS AND METHODS: Patients with OSCC during 2004-2015 were identified in the SEER database, and their clinicopathological, demographic, and survival data were examined. Univariate analysis using Gray's test and the cumulative incidence function was used to evaluate the prognoses of events of interest. The multivariate analysis involved several models, including the Cox proportional hazards, Fine-Gray, and cause-specific (CS) hazard function models, to estimate the hazard functions of competing risks. Hazard ratios were analyzed to identify the reliability of the prognostic factors. RESULTS: Among the 10,400 individuals diagnosed with OSCC, 5,713 died from the ill-ness, and 1,125 died from other causes. The cumulative incidence rate of events of interest was found to be significant for ethnicity, age at diagnosis, histological grade, American Joint Committee on Cancer (AJCC) stage, chemotherapy and surgery status, tumor size, marital status, and local lymph node metastases (p<0.05). The multivariate analysis revealed that ethnicity, histological grade, surgery and chemotherapy status, age at diagnosis, AJCC stage, marital status, and distant metastases were independent prognostic factors in the Cox model (p<0.05). Finally, the Fine-Gray and CS models demonstrated that ethnicity, histological grade, surgery and chemotherapy status, age at diagnosis, AJCC stage, tumor size, marital status, and combination summary stage were all identified as independent prognostic factors (p<0.05). CONCLUSIONS: This study determined the risk factors for OSCC using a competing risk analysis model established by the SEER data-base. The findings can help clinicians understand OSCC better and provide more accurate medical support to affected patients.