In This Video
- Early-stage breast cancer impact approximately 60,000 women in the United States every year
- With increasing concerns about overtreatment, active surveillance programs have been introduced to monitor disease and help patients avoid unnecessary surgery and radiation
- Manisha Bahl, MD, and colleagues have developed an artificial intelligence tool to identify women with low risk disease who are appropriate candidates for the programs
Early-stage breast cancer impact approximately 60,000 women in the United States every year. With increasing concerns about overtreatment, active surveillance programs have been introduced to monitor disease and help patients avoid unnecessary surgery and radiation. In this video, Manisha Bahl, MD, MPH, director of the Breast Imaging Fellowship Program in the Department of Radiology at Massachusetts General Hospital, discusses an artificial intelligence tool, she and colleagues have developed, to identify women with low-risk disease who are appropriate candidates for the programs.
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I'm studying early-stage breast cancer, which affects about 60,000 women in the United States every year and is treated with surgery and radiation. Growing concerns regarding overtreatment of this type of breast cancer have led to active surveillance trials in which surgery and radiation are avoided and imaging is used to monitor disease.
Critical to the success of these active surveillance programs is the careful selection of eligible patients. The purpose of my research is to develop a tool that identifies women with low-risk disease who are appropriate candidates for active surveillance. The tool will combine clinical data, imaging data and pathology data using cutting-edge artificial intelligence, machine learning and deep learning techniques. This work is being done in collaboration with artificial intelligence experts at MIT and is being funded by the National Institutes of Health.
Use of the tool that we're developing in the clinical setting will empower women with early-stage breast cancer to make more informed choices regarding their treatment options. For example, there is a lot of controversy about how to manage high-risk lesions in the breast. This controversy has led to wide variations in patient care with some women undergoing surgery and others undergoing surveillance with imaging. Our Mass General/MIT collaborative team used historical data from more than 1000 women with high-risk lesions at Mass General to develop an AI model. That model can be used to identify which women with high-risk lesions would benefit most from surgery and which women with high-risk lesions could safely be followed with imaging. Use of this tool in the clinical setting could decrease unnecessary breast surgeries and support shared decision-making with regard to surgery versus surveillance my colleagues and I in breast imaging are in close communication with other members of the multidisciplinary care team including the breast surgeons, medical oncologists and radiation oncologists. We work closely with the cancer center and the multidisciplinary care team with weekly tumor boards and daily multidisciplinary conferences to discuss our patients and to provide the best patient care possible.
Learn more about the Division of Breast Imaging
Learn more about the Department of Radiology