Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI

Purpose Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer. Method We conducted a retrospective study including 223 patients diagnosed with invasive ductal breast cancer. Radiomics features were extracted from DCE-MRI using 3D-Slicer software. Two Ki-67 expression cutoff values (20% and 29%) were examined. Patients were divided into training (70%) and test (30%) sets. The Elastic Net method selected relevant features, and five machine-learning models were established. Radiomics models were created from intratumoral, peritumoral, and combined regions. Performance was assessed using ROC curves, accuracy, sensitivity, and specificity. Result For a Ki-67 cutoff value of 20%, the combined model exhibited the highest performance, with area under the curve (AUC) values of 0.838 (95% confidence interval (CI): 0.774–0.897) for the training set and 0.863 (95% CI: 0.764–0.949) for the test set. The AUC values for the tumor model were 0.816 (95% CI: 0.745–0.880) and 0.830 (95% CI: 0.724–0.916), and for the peritumor model were 0.790 (95% CI: 0.711–0.857) and 0.808 (95% CI: 0.682–0.910). When the Ki-67 cutoff value was set at 29%, the combined model also demonstrated superior predictive ability in both training set (AUC: 0.796; 95% CI: 0.724–0.862) and the test set (AUC: 0.823; 95% CI: 0.723–0.911). The AUC values for the tumor model were 0.785 (95% CI: 0.708–0.861) and 0.784 (95% CI: 0.663–0.882), and for the peritumor model were 0.773 (95% CI: 0.690–0.844) and 0.729 (95% CI: 0.603–0.847). Conclusion Radiomics with DCE-MRI can predict Ki-67 expression in invasive ductal breast cancer. Integrating radiomics features from intratumoral and peritumoral regions yields a dependable prognostic model, facilitating pre-surgical detection and treatment decisions. This holds potential for commercial diagnostic tools.