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

• Published in: BMC cancer Vol. 20; no. 1; p. 833
• Main Authors: Xie, Guilan, Wang, Ruiqi, Shang, Li, Qi, Cuifang, Yang, Liren, Huang, Liyan, Yang, Wenfang, Chung, Mei Chun
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.09.2020; BioMed Central; BMC
• Subjects: Adult; Age; Aged; Calibration; Cancer patients; Cancer research; Cancer therapies; Care and treatment; Cervical cancer; Chemotherapy; Comparative analysis; Death & dying; Decision curve analysis; Development and progression; Diagnosis; Female; Follow-Up Studies; Histology; Humans; Kaplan-Meier Estimate; Lymph nodes; Lymphatic system; Medical prognosis; Metastasis; Middle Aged; Neoplasm Staging; Nomogram; Nomograms; Overall survival; Patient outcomes; Patients; Prediction models; Probability; Prognosis; Proportional Hazards Models; Radiation therapy; Retrospective Studies; Risk Factors; ROC Curve; SEER Program; Sociodemographics; Surgery; Survival; Survival Rate; Telecommunications equipment; Uterine Cervical Neoplasms - mortality; Women's health; United States > US; Decision curve analysis; Overall survival; Nomogram; Cervical cancer
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-020-07349-4

Cervical 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. Data 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. Eleven 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. A 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.