Development and validation of a nomogram for predicting recurrence-free survival in endometrial cancer: a multicenter study

• Published in: Scientific reports Vol. 13; no. 1; p. 20270
• Main Authors: Li, Yinuo, Hou, Xin, Chen, Wei, Wang, Shixuan, Ma, Xiangyi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67; 692/499; CA-125 Antigen; Calibration; Cancer; Endometrial cancer; Endometrial Neoplasms; Endometrium; Female; Gynecology; Humanities and Social Sciences; Humans; multidisciplinary; Nomograms; Obstetrics; Patients; Pregnancy; Retrospective Studies; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-47419-8

Recurrence is the main cause of death in patients with endometrial cancer (EC). This study aimed to construct and validate a nomogram to predict the recurrence-free survival of patients with EC. This was a multicenter retrospective study. A total of 812 patients from Wuhan Tongji Hospital were divided into training and validation cohorts, and 347 and 580 patients from People’s Hospital of Peking University and Qilu Hospital of Shandong, respectively, were used for validation. Univariate and multivariate Cox regression analyses were used to construct a nomogram for predicting recurrence-free survival of EC. Calibration curves, receiver operating characteristic (ROC) curves, and consistency indexes (C-indexes) were used to estimate the performance of the model. Decision curve analysis (DCA) curves were used to assess the clinical utility of the model. Age ( P  = 0.013), cancer antigen 125 level ( P  = 0.014), lymphovascular space invasion ( P  = 0.004), International Federation of Gynecology and Obstetrics stage ( P  = 0.034), and P53 ( P  < 0.001) were independently associated with recurrence, and we constructed a nomogram based on these variables. The C-indexes of the validation cohorts were 0.880, 0.835, and 0.875, respectively. The calibration, ROC, and DCA curves revealed that this model had excellent performance and clinical utility. Combining clinical data, clinicopathological factors, serological indicators, and immunohistochemical marks, a multicenter externally verified nomogram with robust performance was constructed to predict the recurrence of patients with EC.