A Bayesian Network Predicting Survival of Cervical Cancer Patients-Based on Surveillance, Epidemiology, and End Results.

This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer for patients who received standard treatments, and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BN) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43749 and 39333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.