Keywords: Breast, Quantitative Imaging, K-means clustering, DCIS, grading
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 ($$$\alpha$$$, $$$A\cdot\alpha$$$, $$$AUC30$$$) 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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords