Baseline multimodal clinical-radiomics-pathomics model predicts mucosal healing in Crohn’s disease after infliximab therapy

Figure 5 The SHapley Additive exPlanations beeswarm plot provides a comprehensive perspective on feature importance. Each row represents a single feature, and the horizontal axis indicates the corresponding SHapley Additive exPlanations (SHAP) value. Individual data points represent patient samples; red indicates higher feature values, while blue represents lower values. A positive SHAP value (> 0) is associated with a reduced likelihood of mucosal healing, whereas a negative value (< 0) is correlated with an elevated probability of mucosal healing. A: This plot illustrates 21 key features, including 4 pathomics features, 16 radiomics features, and 1 clinical feature, along with their combined impacts on the prediction probability. The three most influential features are: Granularity 14 Eosin, wavelet.LHL.glcm.ldmn, and diagnostics.lmage.interpolated.Maximum; B: This plot illustrates 21 key features, including 13 pathomics features, 7 radiomics features, and 1 clinical feature, along with their combined impacts on the prediction probability. The three most influential features are: Granularity 3 Eosin, log.sigma.0.5.mm.3D. firstorder. Mean, and X2Granularity 12 Eosin. They play important roles in the interpretability of the model and feature correlation. Alb: Albumin; FC: Fecal calprotectin; SHAP: SHapley Additive exPlanations.