Automated machine learning predicts liver metastases in patients with early-onset gastroenteropancreatic neuroendocrine tumors.

Background: The incidence of early-onset gastroenteropancreatic neuroendocrine tumors (GEP-NETs) is increasing, with liver metastases often occurring early and adversely affecting prognosis. This study aimed to develop a predictive model for liver metastases detection in patients with early-onset GEP-NETs (<50 years) using an automated machine learning (AutoML) approach. Methods: A retrospective analysis was conducted on patients diagnosed with early-onset GEP-NETs [2000-2021] using data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training set (n=8,983) and a validation set (n=3,819) in a 7:3 ratio. A nomogram-based scoring system was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression. AutoML was applied to build predictive models using gradient boosting machine (GBM), generalized linear model (GLM), deep learning (DL), and distributed random forest (DRF) algorithms. Model performance was assessed using receiver operating characteristic (ROC), calibration, decision curve analysis (DCA), and interpretability tools including SHapley Additive exPlanations (SHAP), partial dependence plots (PDPs), and locally interpretable model-agnostic explanations (LIME) plots. Results: A total of 12,802 patients were included, of whom 1,187 (9.3%) developed liver metastases, comprising 851 (9.5%) and 336 (8.8%) cases in the training and validation sets, respectively. Comparative analyses demonstrated that the AutoML models outperformed traditional logistic regression models, with the GBM algorithm achieving the highest performance. The GBM model achieved an area under the curve (AUC) of 0.961 in the training set and 0.953 in the validation set. Tumor location was identified as the most important predictor in the GBM model, followed by surgery, tumor size, chemotherapy, and T-staging. Conclusions: The AutoML model leveraging the GBM algorithm provides a robust and clinically valuable tool for the early prediction of liver metastases in patients with early-onset GEP-NETs.