Factors influencing disease-free survival after radical endometrial cancer surgery: an analysis of the competitive risk prediction mode.

Abstract

Objective: To investigate the factors influencing disease-free survival (DFS) of patients with endometrial cancer after surgery and construct a competing risk prediction model.

Methods: Clinical data of endometrial cancer patients admitted to the First People's Hospital of Qinzhou City from October 2015 to January 2021 were retrospectively analyzed. A total of 280 patients were included, randomly split into a training set (202 cases) and a validation set (78 cases) in a 7:3 ratio using RStudio software. A Fine-Gray competing risk model was applied to the training set to identify factors associated with reduced postoperative DFS. Based on these factors, a prognostic prediction model was established, and a nomogram was created. The model's performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve and calibration curve.

Results: Multifactorial analysis revealed that age, body mass index (BMI), diabetes mellitus, depth of basal infiltration, cancer antigen 125 (CA125), and human epididymis protein 4 (HE4) were the factors influencing postoperative DFS in endometrial cancer patients (P < 0.05). In the training set, the constructed model showed AUC values of 0.773, 0.802, and 0.858 in predicting 1-, 2-, and 4-year DFS, respectively. In the validation set, the AUC values were 0.923, 0.829, and 0.746, respectively. The C-index in the training set and the validation set was 0.786 and 0.515, respectively. The calibration curve indicated that the predicted cumulative survival probabilities closely matched the actual probabilities in both the training and validation sets.

Conclusions: The Fine-Gray competing risk prediction model is effective in identifying factors influencing postoperative DFS in patients with endometrial cancer. The nomograms derived from this model have a strong predictive value and can help clinicians in identifying high-risk patients and tailoring individualized interventions.