Establishment and validation of a prognostic risk early-warning model for retinoblastoma based on XGBoost

Retinoblastoma (RB) is the most common intraocular malignancy in children, and early detection and treatment are crucial for improving patient outcomes. Conventional treatments, such as enucleation and radiotherapy, have limitations in fully addressing prognosis. This study aimed to establish and validate an early-warning prognostic model for RB based on the XGBoost algorithm to improve the prediction accuracy of the 5-year survival rate in children. A retrospective analysis was conducted on 320 children with RB treated at Changzhi People's Hospital between February 2012 and April 2019. The patients were randomly divided into a training group (n=224) and a validation group (n=96). Clinical data, including age, gender, tumor characteristics, and tumor marker levels, were collected. Prognostic factors were analyzed using XGBoost and Cox regression models, and model performance was evaluated using various statistical methods. No significant differences were observed in baseline data between the two sets (P>0.05). Cox regression analysis identified tumor diameter (P=0.032), IIRC stage (P<0.001), and NSE (P=0.016) as independent prognostic factors. The XGBoost model achieved an area under the curve (AUC) of 0.951 in the training group, significantly higher than the Cox model (P=0.001), while in the validation group, the XGBoost model's AUC was 0.902, with no significant difference compared to the Cox model (P=0.117). The XGBoost model demonstrated high accuracy and clinical utility in predicting the 5-year survival of children with RB. Decision curve analysis (DCA) and calibration curves further confirmed that the XGBoost model offers higher clinical net benefits and superior calibration ability across various thresholds.