Fig. 2

Performance of the fusion model in the internal test set. (A) When comparing the ROAUC performance of six machine learning algorithms within the internal test set, the SVM algorithm significantly outperformed the others. (B) The confusion matrix of the SVM model demonstrates the classification results within the internal test set, with a sensitivity of 0.823 and a specificity of 0.691. (C) There is a significant difference in the predictive probability distribution between high and low Ki-67 expression samples using the SVM model. (D) The clinical decision curve suggests that when the probability of predicting high Ki-67 expression by the SVM model falls between 0.200 and 0.800, the model curve lies above the reference lines for “Treat all” and "Treat none," indicating that within this range, the net benefit of the model is higher than these two simple strategies.