• Guilan Xie, Ruiqi Wang, Li Shang, Cuifang Qi, Liren Yang, Liyan Huang, Wenfang Yang, and Mei Chun Chung.
• Department of Obstetrics and Gynecology, Maternal & Child Health Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, 710061, People's Republic of China.
• Bmc Cancer. 2020 Sep 1; 20 (1): 833.

BackgroundCervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer.MethodsData of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010-2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell's C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models.ResultEleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model.ConclusionA clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research.

21 keywords...

hide…