Jay Alan Baker

As a radiologist in the Division of Breast Imaging, I am interested in studying techniques to better detect and assess breast lesions that may represent breast cancer. The major focus of my research activity involves identifying features of breast lesions on mammography/tomosynthesis, ultrasound and MRI that reliably indicate breast cancer or, equally important, reliably indicate a lesion is not breast cancer and biopsy can be safely avoided. 

Breast cancer is the most common malignancy occurring in women and the second most frequent cause of non-skin cancer deaths among women. Screening mammography programs have repeatedly shown a reduction in the mortality from breast cancer by 30 to 50%. However, breast imaging suffers from a lack of specificity. The result is that 60 to 80% of breast biopsies performed in this country are for benign lesions and are therefore - in retrospect - unnecessary. Because of the overlap in imaging features of benign and malignant lesions, however, these lesions cannot be differentiated without tissue sampling, and the extraordinary number of breast biopsies performed markedly increases the cost of breast cancer prevention programs and is an impediment to breast screening for some women. To overcome this limitation, we are working to identify previously unrecognized features of breast lesions.  Some of these features appear to confirm that a lesion is definitively benign without the need for biopsy.  Other features have identified a particular appearance for breast cancer with features that mimic other benign lesions, thus allowing earlier diagnosis and fewer overlooked breast cancers. We are assessing these features with large reader studies to both determine the accuracy and to confirm that the features can be taught and recognized by radiologists at all levels of breast imaging experience.  If successful, widespread adoption and recognition of these features may greatly reduce the number of women who undergo a needle biopsy for a benign breast lesion.  Conversely, widespread recognition of other features may reduce delays in diagnosis of breast cancer.