Fig. 4

Model interpretability of a combined clinicopathological and radiomics model for the prediction of tumor recurrence with SHAP in the training set. a Variance importance plot that lists the most significant variables in descending order. b Summary plot of feature impact on the decision of the model and interaction between the features in the model. A positive SHAP value indicates an increase in the probability of tumor recurrence. c Decision plot showing how the model predicts tumor recurrence. Starting at the bottom of the plot, the prediction line shows how the SHAP values accumulate from the base value to arrive at the model’s final score at the top of the plot and how each feature contributes to the overall prediction of tumor recurrence. d Force plot of a representative case of a patient with tumor recurrence. Red arrows represent feature effects that drive the prediction value higher, and blue arrows are those effects that drive the prediction value lower. Each arrow’s size represents the magnitude of the corresponding feature’s effect. Note that the extent of resection, 90th percentile from T1C, and Ki-67 labeling index largely push the model prediction score higher than the base value