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Updated: Nov 7, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with
Guilan Xie1,2, Cuifang Qi1, Wenfang Yang1
1Department of Obstetrics and Gynecology, Maternal and Child Health Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Background:
The incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with hysterectomy.
Methods:
Patients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3- and 5-year incidences of endometrial cancer-specific mortality. Harrell's C-index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility.
Results:
A total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C-index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854-0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3-year prediction and between 2% and 67% for 5-year prediction, the competing risk nomogram was of good clinical utility.
Conclusions:
A competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients.
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