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 includes both basic science and clinical approaches to developing computer-aided diagnosis, novel digital imaging techniques such as digital breast tomosynthesis, advanced ultrasound techniques, and MRI to detect and classify breast lesions.
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 60%. 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. Our work has focused on building computer aided classification systems to assist the radiologist in differentiating benign from malignant breast lesions without the use of invasive biopsies. In our systems, imaging features of breast lesions are combined using artificial intelligence techniques with information such as the patient's age, family history, and change from prior imaging studies to determine the likelihood that a particular lesion is malignant. This information can guide the radiologist to offer follow-up imaging rather than biopsy for those women with lesions that are very unlikely to be breast cancer.
A new focus of research in our lab is the development of full field digital mammography (FFDM) systems that acquire mammographic information using a digital detector rather than film. The advantage of this technique is the possibility of developing advanced applications such as tomosynthesis, contrast enhanced mammography, and dual energy imaging, as well as clinical advantages such as the ability to manipulate the appearance of the image after acquisition and improve film storage and transport. We are collaborating with a major imaging equipment manufacturer to develop both their commercial FFDM system and to develop tomosynthesis using that system. Tomosynthesis is a technique in which several low dose X-ray images of the breast are obtained at various angles, and thin tomographic slices of the breast are reconstructed. This technique removes the problem of overlapping breast tissue, making detection of breast lesions easier, and, in theory, improving the sensitivity of mammography.
Other efforts in our lab have focused on evaluation of elastography in breast ultrasound. Elastography uses ultrasound systems to determine the stiffness of a breast lesion, often by applying a small burst of sound energy toward a breast mass and measuring changes in the shape of the mass. Since most breast cancers are firm, they will deform less than normal breast tissue or benign masses. Our preliminary work to-date suggests that elastography systems are not sufficiently accurately to safely avoid biopsy of a suspicious breast lesion. However, elastography systems may be aid in the accurate diagnosis of breast cysts and assist in avoiding unnecessary cyst aspirations.
Finally, we are also studying the use of spectroscopy in breast MRI. MR spectroscopy provides information on the chemical makeup of a small volume of breast tissue. Breast cancers often contain a substance called choline while normal tissue and benign lesions do not. We are studying ways to assess the level of choline in breast cancers before and shortly after administration of chemotheraphy to determine whether a patient’s cancer will respond to a particular chemotherapy. Although these studies are only in preliminary stages, if successful they may help clinicians determine whether a particular chemotherapy regimen is likely to be successful.
Education and Training
- University of Pennsylvania, B.A. 1988
- Duke University, M.D. 1992
- Duke University, Resident, Radiology
Selected Grants and Awards
- Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS
- Machine learning and collaborative filtering tools for personalized education in digital breast tomosynthesis
- Preoperative Breast Radiotherapy: A Tool to Provide Individualized and Biologically-Based Radiation Therapy
- (PQC3) Genomic Diversity and the Microenvironment as Drivers of Metastasis in DCIS
- Improved education in digital breast tomosynthesis using machine learning and computer vision tools
- Combined breast MRI/biomarker strategies to identify aggressive biology
- Tension-Stat3-miR-mediated metastasis
- (PQA5) 'Dose and Mechanisms of Exercise in Breast Cancer Prevention'
- 3D Digital Breast Phantoms For Multimodality Research
- Information-Theoretic Based CAD in Mammography
- Tomosynthesis for Improved Breast Cancer Detection
- Reducing Benign Breast Biopsies with Computer Modeling
- FDG-PEM Detection - Characterization of Breast Cancer
- Breast Elemental Composition Imaging
- Resolution Requirements for Mammographic Displays
- Predicting Breast Cancer With Ultrasound and Mammography
- Improved Diagnosis of Breast Microcalcification Clusters