Artificial intelligence (AI) and machine learning tools have received a lot of attention recently, with the majority of discussions focusing on proper use. However, this technology has a wide range of practical applications, from predicting natural disasters to addressing racial inequalities and now, assisting in cancer surgery.

A new clinical and research partnership between the UNC Department of Surgery, the Joint UNC-NCSU Department of Biomedical Engineering, and the UNC Lineberger Comprehensive Cancer Center has created an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery. Their findings were published in Annals of Surgical Oncology.

“Some cancers you can feel and see, but we can’t see microscopic cancer cells that may be present at the edge of the tissue removed. Other cancers are completely microscopic,” said senior author Kristalyn Gallagher, DO, section chief of breast surgery in the Division of Surgical Oncology and UNC Lineberger member. “This AI tool would allow us to more accurately analyze tumors removed surgically in real-time, and increase the chance that all of the cancer cells are removed during the surgery. This would prevent the need to bring patients back for a second or third surgery.”

During surgery, the surgeon will resect the tumor (also referred to as a specimen) and take a small amount of surrounding healthy tissue in an attempt to remove all of the cancer in the breast. The specimen is then photographed using a mammography machine and reviewed by the team to make sure the area of abnormality was removed. It is then sent to pathology for further analysis.

The pathologist can determine whether cancer cells extend to the specimen’s outer edge, or pathological margin. If cancer cells are present on the edge of the tissue removed, there is a chance that additional cancer cells still remain in the breast. The surgeon might have to perform additional surgery to remove additional tissue to ensure the cancer has been completely removed. However, this can take up to a week after surgery to process fully, while specimen mammography, or photographing the specimen with an X-ray, can be done immediately in the operating room.

To “teach” their AI model what positive and negative margins look like, researchers used hundreds of these specimen mammogram images, matched with the final specimen reports from pathologists. To help their model, the researchers also gathered demographic data from patients, such as age, race, tumor type, and tumor size.

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