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

Figure 1 Flowchart of patient selection and dataset allocation. A total of 823 patients were initially screened from three centers. After applying exclusion criteria, 211 patients from center 1 were allocated into the training (n = 150) and validation (n = 61) sets, while 100 patients from centers 2 and 3 were used as the independent test set. AC: Acute cholecystitis; CT: Computed tomography; PC: Percutaneous cholecystostomy; LC: Laparoscopic cholecystectomy; ASC: Acute suppurative cholecystitis.

Figure 2 Workflow of model development. Radiomic features were extracted using Pyradiomics after manual segmentation. Feature selection involved t-test, Pearson correlation filtering, and least absolute shrinkage and selection operator regression. Radiomics and clinical models were constructed using logistic regression. Stacking strategy was used to integrate outputs from both models into a fusion model. WBC: White blood cells; GB: Gallbladder; STB: Serum total bilirubin; NE: Neutrophil granulocytes; UCB: Unconjugated bilirubin; LASSO: Least absolute shrinkage and selection operator; SHAP: SHapley Additive exPlanations.

Figure 3 Receiver operating characteristic curves, decision curve analysis plots and, calibration analysis for the three models in the training, test, and external validation cohorts. A: Panels illustrate the diagnostic performance (area under the receiver operating characteristic curve) of the clinical model, radiomics model, and fusion model across different cohorts; B: Panels compare the net benefit of each model across the cohorts; C: Panels show the agreement between predicted probabilities and observed outcomes for each model. The fusion model consistently outperformed the individual models in all cohorts, indicating the complementary value of clinical and radiomics features. AUC: Area under the receiver operating characteristic curve.

Figure 4 The SHapley Additive exPlanations beewram plots of the radiomics model. The X-axis shows SHapley Additive exPlanations values representing the magnitude and direction of feature contributions. Each dot is a sample, colored by the feature value (red high, blue low), helping interpret feature importance and effects. All features used in the radiomics model are derived from non-contrast computed tomography images. The features include: Original first-order and shape features, texture features from gray level size zone matrix, gray level dependence matrix, gray level run length matrix, neighboring gray tone difference matrix, and local binary pattern families, and filtered/transformed features using logarithm, gradient, wavelet, or square root operations. The Y-axis is limited to the range (-2, 2) to focus on the main impact range and improve plot readability by reducing the influence of extreme values. SHAP: SHapley Additive exPlanations.

Figure 5 The SHapley Additive exPlanations force plot. Red features indicate an increased risk of acute suppurative cholecystitis (ASC), while blue features indicate a decreased risk. A: For patients with ASC, the model predicts a 97.5% probability of a positive result; B: For patients without ASC, the model predicts a 84.1% probability of a negative result; C: For patients with ASC, the model predicts a 58.4% probability of a negative result; D: For patients without ASC, the model predicts a 54.8% probability of a positive result.

Figure 6 Visualization of the fusion model’s predicted probabilities in the training set based on the predicted probabilities from clinical model and radiomics model. Each point represents a sample, with the X-axis indicating the probability predicted by clinical model and the Y-axis indicating the probability predicted by radiomics model. The color gradient reflects the predicted probability from the C model, which integrates both modalities, with blue indicating lower probability and red indicating higher probability. Circle and square markers represent negative and positive ground-truth labels, respectively.