Conditional Survival of Advanced Epithelial Ovarian Cancer: A Real-World Data Retrospective Cohort Study From the SEER Database

Objective: To analyze conditional survival (CS) in patients with advanced epithelial ovarian cancer (EOC) and investigate prognostic factors that affect the CS rate to provide more accurate survival information.Methods: Patients with advanced EOC between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. CS analysis was performed to depict exact survival for patients who had already survived a specific number of years. Cox proportional hazards regression was performed to ascertain the individual contribution of factors associated with actuarial overall survival (OS) at diagnosis and CS at 1, 3, and 5 years after diagnosis.Results: Of 11,773 patients, OS decreased from 32.2% at 6 years to 25.1% at 8 years, while the corresponding 5 year CS (CS5) increased from 37.5% at 1 year to 43.9% at 3 years. Subgroup analysis stratified by clinicopathological characteristics showed that CS5 was always higher than the corresponding actuarial survival (all Δ > 0). Based on multivariate analysis at diagnosis, age, race, marital status, histological type, tumor grade, size, T stage, M stage, surgery, radiation therapy, and chemotherapy were independent prognostic factors for OS. Five years after diagnosis, however, only age, histological type, tumor grade, and laterality were persistently significant independent prognostic factors (all P <0.05). Furthermore, patients with poor pathological prognostic factors achieved greater improvements in CS5 rates, and the survival gaps between OS and CS were more obvious.Conclusion: CS of advanced EOC was dynamic and increased over time. Age, histology, tumor grade, and laterality were significant prognostic factors even 5 years after diagnosis. Thus, the availability of updated prognoses at various time points will allow clinicians to better guide their patients.

Dynamic survival analysis of gastrointestinal stromal tumors (GISTs): a 10-year follow-up based on conditional survival

Background The prognosis of patients with gastrointestinal stromal tumors (GISTs) is generally evaluated at the time of diagnosis but does not reflect the survival dynamics of patients in the future. Therefore, the purpose of this article was to evaluate the conditional survival (CS) of Chinese patients with GISTs after radical resection. Methods This retrospective study included 451 patients who underwent radical surgery for GISTs. A Cox proportional hazard model was used to evaluate the prognostic factors of disease-free survival (DFS). The 3-year conditional DFS (CDFS3) of patients who survived for x years was expressed as CDFS3=DFS(x + 3)/DFS(x). Results The traditional 3-year DFS rate decreased gradually from 94.0% at 3 years to 77.3% at 7 years, while the CDFS3 rate increased from 94.0 to 95.2% over the survival time of the patients. In addition, classic clinicopathological prognostic factors had different effects on CDFS3, with changes observed in survival time, but these effects were only slight or moderate (|d|<0.5). Although multivariate analysis showed that age, sex, mitotic index and tumor rupture were independent risk factors for DFS at baseline, all adverse prognostic factors, except for the mitotic index, lost their predictive significance at 5 years after operation. When the Modified NIH criteria were included, the risk staging was found to be an independent risk factor for recurrence or death. Time-dependent Cox regression analysis showed that the modified NIH criteria independently affected the recurrence or death of GIST patients within 2 years after operation. Conclusion CS provides detailed dynamic survival information about Chinese patients with primary resected GISTs. The mitotic index is of great clinical significance for the monitoring and follow-up of patient populations with a high risk of tumor recurrence or death until 5 years after surgery.

Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available

We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the sake of illustration, we present an application concerning patients hospitalized with COVID-19 in Galicia (Spain) during the first outbreak of the epidemic.