Construction and validation of nomogram prognostic model for predicting survival in hepatoblastoma patients: a...

For patients with hepatoblastoma (HB), current staging system is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. A retrospective analysis of 424 HB patients was performed from 2004 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to screen for variables. The identified variables were used to build survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, and receiver operating characteristic (ROC) curve. The Cox regression analysis identified six variables affecting overall survival (OS) in HB patients, including race, tumor size, lymph node involvement, distant metastases, surgery and chemotherapy. And the Cox regression analysis identified five variables including race, lymph node involvement, distant metastases, surgery, and chemotherapy that affect cancer-specific survival (CCS) in HB patients. In the training cohort, the C-index of the nomogram in predicting the OS was 0.791 [95% confidence intervals (95% CI) 0.717-0.865], CSS was 0.805(95% CI 0.728-0.882). In the validation cohort, the C-index of the nomogram in predicting the OS was 0.712 (95% CI 0.511-0.913), the CSS was 0.751 (95% CI 0.566-0.936). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1-, 3-, and 5-year OS were 0.842 (95% CI 0.739-0.944), 0.759 (95% CI 0.670-0.849), and 0.770 (95% CI 0.686-0.852), respectively. In the validation cohort, the AUC values for prediction of the 1-, 3-, and 5-year OS were 0.920 (95% CI 0.806-1.034), 0.863 (95% CI 0.750-0.976), and 0.844 (95% CI 0.721-0.967), respectively. Two nomogram models were developed and validated in this study which provided accurate prediction of the OS and CSS in HB patients. The constructed models can be used for predicting survival outcomes and guide treatment for HB patients.