Machine learning prediction model for distant metastasis at diagnosis in pediatric neuroblastoma: development, interpretation, and temporal validation.

Background: Distant metastasis (DM) at diagnosis is a key determinant of risk stratification, treatment intensity and prognosis in pediatric neuroblastoma (NB). However, an interpretable risk-estimation tool based only on routinely available variables at diagnosis or during baseline diagnostic/pathological assessment and assessed through temporal validation remains limited. This study aimed to develop and temporally validate an interpretable machine-learning model for estimating DM risk at diagnosis. Methods: We constructed a population-based pediatric cancer registry cohort of patients aged 0-19 years with NB or ganglioneuroblastoma (GNB). The primary outcome was DM at diagnosis. Candidate predictors were restricted to demographic and tumor-related variables available at diagnosis or during baseline diagnostic/pathological assessment, while surgery, radiotherapy and chemotherapy were excluded to reduce information leakage. Feature selection integrated univariable logistic regression, least absolute shrinkage and selection operator (LASSO) regression, Boruta and prespecified clinical retention rules. Patients diagnosed during 2000-2017 formed the development cohort, which was stratified 7:3 into training and internal validation sets; patients diagnosed during 2018-2023 formed the temporal validation cohort. Seven algorithms were compared. Shapley Additive Explanations (SHAP) analysis and decision curve analysis (DCA) were used to assess model interpretability and net benefit. Results: The primary cohort included 3,837 patients, of whom 1,998 (52.1%) had DM at diagnosis. Light gradient boosting machine (LightGBM) was selected as the final model. It achieved an area under the receiver operating characteristic curve (AUC) of 0.775 [95% confidence interval (CI): 0.743-0.804] in internal validation and 0.802 (95% CI: 0.774-0.829) in temporal validation with Brier scores of 0.193 and 0.181, respectively. In temporal validation, sensitivity and specificity were 0.750 and 0.729. DCA suggested net benefit across a range of threshold probabilities. SHAP analysis identified laterality, age at diagnosis, histologic subtype, tumor size and regional lymph nodes examined as leading model-contributing features. A prototype web-based calculator was developed for research-oriented visualization of model predictions. In an exploratory secondary analysis, lung metastasis was associated with higher 36-month mortality odds among patients with DM. Conclusions: A LightGBM model based on routinely available variables at diagnosis or during baseline diagnostic/pathological assessment showed stable discrimination and calibration for estimating DM risk in pediatric NB. This tool may support research-oriented early risk stratification, but independent external and prospective validation is required before clinical use.