Development and validation of nomogram-based models for personalized survival assessment in pediatric hepatoblastoma patients.

Abstract

Hepatoblastoma (HB) is a prevalent form of liver cancer in pediatric patients, characterized by an embryonal malignant tumor. In the current study, a clinical prediction model was developed; that can effectively assess the likelihood of a patient's survival with HB. Data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of HB between 2010 and 2019 were used in this retrospective research. Information on clinicopathologic characteristics, therapeutic interventions, and survival outcomes were included in the data. The HB patients were randomly assigned to the training or validation cohort in a 7:3 ratio. Using univariate and multivariate Cox proportional hazards regression models, the prognostic indicators for overall survival (OS) and cancer-specific survival (CSS) were identified. The area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and concordance index (C-index) were used to evaluate the accuracy and calibration of these models. The clinical utility of the models was examined using decision curve analysis (DCA). The multivariate Cox regression analysis revealed multiple autonomous prognostic determinants for the OS and CSS, including age, surgical interventions, and chemotherapy administration. Significantly, tumor size was found to be a strong predictor of OS. AUC values of 0.915, 0.846, and 0.847 for 1-, 3-, and 5-year OS, respectively, indicated that the nomogram-based models were highly accurate at predicting outcomes. Similarly, the AUC values for CSS were 0.871, 0.814, and 0.825. The C-index measurements, which quantify the discriminatory performance of the models, produced CSS values of 0.836 and OS values of 0.864. Furthermore, the calibration plots accurately represented the actual survival rates. Concurrently, the DCA had validated the clinical relevance of the nomogram-based models. The present study successfully developed and validated user-friendly nomogram-based models, allowing for accurate assessment of OS and CSS in pediatric HB patients. These tools enable personalized survival predictions, enhance risk stratification, and strengthen clinical decision-making for managing HB.