Tree-based models such as random forest and XGBoost achieved the best performances (AUC 0.762 [0.726–0.795] and 0.760 [0.724–0.794], respectively) (Table 4), and feature importance was determined with Shapley values, where tumor size, Ki-67, and patient age were the features with the highest predictive power [10]. Here, MKI67 is linked to neoplasm.