Gradient boosting model predicts liver metastasis in pancreatic neuroendocrine tumors.

A study utilizing data from 7,463 pancreatic neuroendocrine tumor (PanET) patients developed a gradient boosting machine (GBM) model, marking it as the most effective tool for predicting liver metastasis. The model achieved an AUC of 0.937 and an accuracy of 0.87, highlighting independent risk factors like T-stage, N-stage, and previous treatments. It culminated in a user-friendly web-based calculator to assist clinicians in making informed treatment decisions based on individual patient risk assessments.