A Multimodal Machine Learning Model Integrating Ultrasound and Serological Biomarkers for Non-Invasive Prediction of Gallbladder Polyp Malignancy: Development, Validation, and Clinical Translation

Background: Differentiating benign from malignant gallbladder polyps (GBPs) is critical for clinical decisions. Pathological biopsy, the gold standard, requires cholecystectomy, underscoring the need for non-invasive alternatives. Methods: This retrospective study included 202 patients (50 malignant, 152 benign) who underwent cholecystectomy (2018–2024) at Fujian Provincial Hospital. Ultrasound features (polyp diameter, stalk presence), serological markers (neutrophil-to-lymphocyte ratio [NLR], CA19-9), and demographics were analyzed. Patients were split into training (70%) and validation (30%) sets. Ten machine learning (ML) algorithms were trained; the model with the highest area under the receiver operating characteristic curve (AUC) was selected. SHapley Additive exPlanations (SHAP) identified key predictors. Models were categorized as Clinical (ultrasound + age), Hematological (NLR + CA19-9), and Combined (all five variables). ROC, Precision-Recall (PR), calibration, and Decision Curve Analysis (DCA) curves were generated. A web-based calculator was developed. Results: The Extra Trees model achieved the highest AUC (0.97 in training, 0.93 in validation). SHAP analysis highlighted polyp diameter, sessile morphology, NLR, age, and CA19-9 as top predictors. The Combined Model outperformed Clinical (AUC 0.89) and Hematological (AUC 0.68) models, with balanced sensitivity (66–54%), specificity (94–93%), and accuracy (87–83%). Conclusion: This ML model integrating ultrasound and serological markers accurately predicts GBP malignancy. The web-based calculator facilitates clinical adoption, potentially reducing unnecessary surgeries.