A post-recurrence survival‑predicting indicator for cervical cancer from the analysis of 165�patients who developed recurrence

Abstract

Abstract. The aim of the present study was to estimate the post-recurrence survival (PRS) of patients with relapsed uterine cervical cancer (RUCC). In addition, clinicopathological indicators that influenced PRS were investigated. Between 1998 and 2014, of 740 patients with cervical cancer, 165 patients experienced recurrence (recurrence rate, 22.3%), and 83 patients succumbed to the disease within a median follow-up of 34.3 months. A total of 151 stage Ib-IV patients who experienced recurrence after initial treatment for cervical cancer at our institute were analyzed. Uni-and multivariate analyses were performed using the Kaplan Meier method, and Cox regression model. The median age was 55 years (range, 20-88 years). In all, 80 patients succumbed to the disease. The median PRS time of all the patients was 28.4 months. The 1-, 3-, and 5-year PRS rates of patients were 75.1, 41.9, and 32.1%, respectively. In addition, the median survival period in patients who had received surgery as an initial treatment was significantly longer compared with that in patients who had not previously undergone surgery (36.7 vs. 23.3 months, respectively; P=0.0338). Following the univariate analysis, the median PRS in patients with in-and out-field recurrence was 12.6, and 45.9 months, respectively (P<0.0001). Furthermore, in the multivariable analysis, the recurrence site was a significant prognostic indicator of PRS [(In-field vs. Out-field); hazard ratio, 2.848; 95% confidence interval, 1.707-4.738; P<0.0001]. The long-term clinical outcome of patients with RUCC was poor. In particular, the in-field recurrence was identified to be associated with poor post-recurrence oncological outcome in patients with RUCC.

Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study

“…But for the prognosis of cervical cancer, only a few studies applied it to visualize prediction models [10,11]. In previous studies, most were based on small sample size cohorts, which might reduce the robustness and generalizability of the nomograms [10,26]. Our nomogram was based on a cohort with large sample size, and it guaranteed the reliability and generality of the result.…”

Prognostic Value of Restaging F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography to Predict 3-Year Post-Recurrence Survival in Patients with Recurrent Gastric Cancer after Curative Resection

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Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

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