Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer

Rationale and Objectives Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions. Materials and Methods Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability. Results In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively. Conclusion Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer. Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions. Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability. In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively. Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer.