Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis

BACKGROUND Acute suppurative cholecystitis (ASC) is a critical stage in the progression of acute cholecystitis. ASC indicates an escalation of local inflammation in the gallbladder from mild to significant. The surgical difficulty and mortality of laparoscopic cholecystectomy will increase significantly. AIM To develop a model integrating clinical characteristics and computed tomography (CT) radiomics features to improve the predictive performance of ASC. METHODS Patients diagnosed with acute cholecystitis were retrospectively recruited from three independent centers. Patients were grouped into purulent and non-purulent phases based on the results of percutaneous cholecystostomy or laparoscopic cholecystectomy. Visual analysis of radiologic features combined with clinical information established a clinical model. Radiomics features were extracted from CT images. A radiomics model was extracted from these features. Then a fusion model was built by using a stacking ensemble strategy to integrate clinical and radiomics models. RESULTS Of 311 patients were included (mean ± SD age, 66 ± 15, 154 men; center 1, training and validation dataset; centers 2 and 3, test dataset; training dataset, n = 150; validation dataset, n = 61; test dataset, n = 100). Model performance was evaluated with the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) reveals the importance of radiomics features. In the test dataset, the fusion model better predicted ASC than the clinical model and radiomics model (AUC = 0.82 vs 0.75 vs 0.76, P < 0.05), with similar specificity (83.1% vs 87.7% vs 73.9%) and higher sensitivity (71.4% vs 62.9% vs 45.7%). In addition, SHAP analysis identified logarithm glszm ZoneEntropy as the main predictor for the radiomics model. CONCLUSION The clinical-radiomics model constructed based on the stacking ensemble strategy could significantly improve ASC predictive accuracy.