Date of Award
8-1-2021
Document Type
Thesis
Degree Name
Master of Medical Science (MMSc)
First Advisor
Reni Butler, MD
Abstract
Triple-negative breast cancer is a subtype of invasive ductal carcinoma that lacks expression of estrogen, progesterone, and human epidermal growth factor 2 receptors. Triple-negative cancers are associated with poorer prognosis partly due to their benign imaging features, leading to possible delay in diagnosis. Computer-aided diagnosis, which provides radiologists with a preliminary assessment of a breast lesion based on artificial intelligence training, is a proposed technique that may improve cancer detection with ultrasound. However, very few studies investigated its use in the diagnosis of triple-negative breast cancers. In this study, we will assess the efficacy of computer-aided diagnosis in improving the diagnostic accuracy of ultrasound for triple-negative breast cancers. Using a blinded crossover retrospective review, we will compare reader accuracy of radiologists evaluating triple-negative breast cancers with computer-aided diagnosis versus standard unaided interpretation. This study may provide support for using this technology in clinical practice to help diagnose triple-negative breast cancers.
Recommended Citation
Burick, Isabel, "Computer Aided Diagnosis with Ultrasound to Improve Detection of Triple Negative Breast Cancer" (2021). Yale School of Medicine Physician Associate Program Theses. 93.
https://elischolar.library.yale.edu/ysmpa_theses/93