Develop and validate nomogram to predict cancer-specific survival for patients with testicular yolk sac tumors - Li, Maoxian; Wang, Jinkui; Li, Jinfeng; Zhang, Yongbo; Zhao, Xing; Lin, Yang; Deng, Changkai; Li, Fulin; Peng, Qiang (Frontiers in Public Health, 2022)
Purpose: Testicular yolk sac tumor is a rare malignant germ cell tumor that mainly occurs in young men. Due to the low incidence of yolk sac tumors, there is a lack of prospective cohort studies with large samples. We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with testicular yolk sac tumor (TYST). Materials and Methods: Patient information was downloaded from the Surveillance, Epidemiology and End Results (SEER) database. We enrolled all patients with TSYT from 2000 to 2018, and all patients were randomly divided into a training set and a validation set. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors for patients. We constructed a nomogram based on the multivariate Cox regression model to predict 1-, 3-, and 5-year CSS in patients with TSYT. We used a series of validation methods to test the accuracy and reliability of the model, including the consistency index (C-index), calibration curve and the area under the subject operating characteristic curve (AUC). Results: 619 patients with TYST were enrolled in the study. Univariate and multivariate Cox regression analysis showed that age, T stage, M stage and chemotherapy were independent risk factors for survival. A nomogram was constructed to predict the patient's CSS. The C-index of the training set and the validation set were 0.901 (95%CI: 0.859-0.847) and 0.855 (95%CI: 0.865-0.845), respectively, indicating that the model had excellent discrimination. The AUC showed the same results. The calibration curve also indicated that the model had good accuracy. Conclusions: In this study, we constructed the nomogram for the first time to predict the CSS of patients with TYST, which has good accuracy and reliability and can help doctors and patients make clinical decisions.