A Nomogram for Predicting Progression-free Survival in Patients with Endometrial Cancer

Abstract

AimsEndometrial cancer is one of the most widely known gynaecological malignancies that lacks a prognostic prediction model. This study aimed to develop a nomogram to predict progression-free survival (PFS) in patients with endometrial cancer. Materials and methodsInformation for endometrial cancer patients diagnosed and treated from 1 January 2005 to 30 June 2018 was collected. The Kaplan–Meier survival analysis and multivariate Cox regression analysis were carried out to determine the independent risk factors and a nomogram was constructed by R based on analytical factors. Internal and external validation were then carried out to predict the probability of 3- and 5-year PFS. ResultsIn total, 1020 patients with endometrial cancer were included in the study and the relationship between 25 factors and prognosis was analysed. Postmenopause (hazard ratio = 2.476, 95% confidence interval 1.023–5.994), lymph node metastasis (hazard ratio = 6.242, 95% confidence interval 2.815–13.843), lymphovascular space invasion (hazard ratio = 4.263, 95% confidence interval 1.802–10.087), histological type (hazard ratio = 2.713, 95% confidence interval 1.374–5.356), histological differentiation (hazard ratio = 2.601, 95% confidence interval 1.141–5.927) and parametrial involvement (hazard ratio = 3.596, 95% confidence interval 1.622–7.973) were found to be independent prognostic risk factors; these factors were selected to establish a nomogram. The consistency index for 3-year PFS were 0.88 (95% confidence interval 0.81–0.95) in the training cohort and 0.93 (95% confidence interval 0.87–0.99) in the verification set. The areas under the receiver operating characteristic curve for the 3- and 5-year PFS predictions are 0.891 and 0.842 in the training set; the same conclusion also appeared in the verification set [0.835 (3-year), 0.803(5-year)]. ConclusionsThis study established a prognostic nomogram for endometrial cancer that provides a more individualised and accurate estimation of PFS for patients, which will help physicians make follow-up strategies and risk stratification.