What type of study is this?

This is a Quantitative Study study.

September 16, 2025

Cluster analysis of quantitative ultrafast DCE-MRI for DCIS grading and upgrading without tracing lesion region-of-interest

Key Points

K-means clustering analysis identifies distinct DCIS grades for improved treatment decision-making.
Significant kinetic parameters were higher in patients with DCIS, correlating with aggressive cancer features.
The analysis of ultrafast DCE-MRI can automatically segment DCIS lesions from normal tissue effectively.
These findings suggest potential for personalized treatment strategies based on DCIS grading and characteristics.

Abstract

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.

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