Abstract:

Objective: To analyze the relevant influencing factors of tumor-specific survival in patients with ovarian yolk sac tumor (OYST) and construct a nomogram prediction model for the tumor-specific survival rate of OYST patients. Methods: A total of 358 patients diagnosed with OYST from January 2000 to December 2020 were screened from the SEER database. They were randomly divided into a training set (266 cases) and a validation set (92 cases) at a ratio of 3:1. Univariate and multivariate competing risk analyses were used to identify the independent influencing factors of tumor-specific survival. A nomogram prediction models for the 1-year, 3-year, and 5-year tumor-specific survival rates was constructed. The discrimination, accuracy, and practicality of the model were evaluated by the concordance index (C-index), area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis. A risk stratification system was established to divide the patients into high- and low-risk groups, and the Kaplan-Meier curve was used to analyze the survival differences between the two groups. Results: Multivariate competing risk analysis showed that age, surgery, and regional lymph node dissection were independent influencing factors for tumor-specific survival in OYST patients. Based on this, a nomogram prediction model was constructed. The C-indices of the training set and validation set were 0.829 (95%CI: 0.825-0.833) and 0.808 (95%CI: 0.804-0.812), respectively. The AUCs for predicting the 1-year, 3-year, and 5-year tumor-specific survival rates were 0.927, 0.833, 0.815 and 0.982, 0.880, 0.745, respectively. The Calibration curve and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted and actual tumor-specific survival rates of OYST patients. The clinical decision analysis indicated that the model had certain clinical practicality. The Kaplan-Meier curve showed that the tumor-specific survival rate of the high-risk group was significantly lower than that of the low-risk group (P<0.000 1). Conclusions: The constructed nomogram prediction model for the tumor-specific survival rate of OYST patients has good discrimination and accuracy, which can help clinicians evaluate the prognosis of patients and develop individualized treatment plans to improve the prognosis of patients.

Key words: Ovarian neoplasms, Endodermal sinus tumor, Survival rate, Nomograms, SEER program, Prognosis, Risk adjustment