Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering and Computer Science
First Advisor
Debasis Mitra
Second Advisor
Venkat Keshav Chivukula
Third Advisor
Siddhartha Bhattacharyya
Fourth Advisor
Brian A. Lail
Abstract
Early detection of breast cancer significantly influences patient outcomes. Dynamic Contrast-Enhanced Ultrasound (DCE-US) has shown promise in early detection by visualizing tumor vascularity and perfusion dynamics in real-time. This study evaluates the efficacy of DCE-US in distinguishing four stages of cancer progression: normal, hyperplasia, ductal carcinoma in situ (DCIS), and invasive cancer, using a transgenic mouse model that mimics human breast cancer. Ultrasound burst pulses, while commonly used to remove unbound contrast agents, can potentially damage human tissues. Using the pre-pulse data helps mitigate this risk, ensuring safer and more reliable measurements. A VEGFR2-targeted microbubble contrast agent was injected, and the analysis was conducted using Non-negative Matrix Factorization (NMF), focusing on pre-pulse data. Our results revealed significant p-values that statistically distinguished all combinations of cancer stages except for normal versus hyperplasia and DCIS versus invasive cancer. Additionally, by considering normal and hyperplasia as benign, and DCIS and invasive as malignant, we applied a non-parametric regression model to define a threshold differentiating benign from malignant tissues, achieving robust specificity and sensitivity metrics. These findings underscore the potential of NMF-enhanced DCE-US for accurate tumor characterization and early breast cancer detection, all the while avoiding the use of harmful ultrasound burst pulses to remove unbound tracers. This approach holds significant promise for clinical applications
Recommended Citation
Khamar, Rutvi, "Early Breast Cancer Detection with Ultrasound Data using NMF" (2025). Theses and Dissertations. 1562.
https://repository.fit.edu/etd/1562