Motivation: Precise grading of ductal carcinoma in situ (DCIS) breast cancer is crucial for selecting the most effective treatment approach and forecasting patient outcomes. Goal (s): This study assesses whether k-means clustering analysis of kinetic ultrafast dynamic-contrast-enhanced MRI (DCE-MRI) could differentiate DCIS grades in 72 patients. Approach: Using k-means clustering (K=5), DCIS lesions were effectively separated from normal tissue. Results: Key kinetic parameters (, A, AUC30) were significantly higher in patients with DCIS and invasive cancer. AUC30 also correlated with DCIS grade, with higher values in high-grade cases. This method could automatically segment DCIS to identify aggressive DCIS and guide treatment strategies. Impact: K-means clustering analysis of ultrafast DCE-MRI can help identify DCIS, differentiate between low- and high-grade DCIS and identify invasive potential, and facilitate personalized treatment by guiding decisions on aggressive treatment versus surveillance for breast cancer patients.
Ren et al. (Tue,) studied this question.