Model developed at MIT's Computer Science and Artificial Intelligence Laboratory could reduce false positives and unnecessary surgeries. Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they're still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. One common cause of false positives are so-called "high-risk" lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time.

Breast cancer is a condition that claims the lives of 40,000 women every year in the U.S. alone. While mammograms are useful, not they're not perfect and often give off false positives, leading to unneeded biopsies and unnecessary surgery. High-risk lesions are one common cause of false positives. When tested with a biopsy needle they show abnormal cells and they appear as suspicious on mammograms too. So then, what can we do to improve these methods and ultimately save lives?

What is the background for this study? What are the main findings? Response: Image-guided biopsies that we perform based on suspicious findings on mammography can yield one of three pathology results: cancer, high-risk, or benign. Most high-risk breast lesions are noncancerous, but surgical excision is typically recommended because some high-risk lesions can be upgraded to cancer at surgery. Currently, there are no imaging or other features that reliably allow us to distinguish between high-risk lesions that warrant surgery from those that can be safely followed, which has led to unnecessary surgery of high-risk lesions that are not associated with cancer.

TUESDAY, Oct. 17, 2017 (HealthDay News) -- Machines armed with artificial intelligence may one day help doctors better identify high-risk breast lesions that might turn into cancer, new research suggests. High-risk breast lesions are abnormal cells found in a breast biopsy. These lesions pose a challenge to doctors and patients. And although they can develop into cancer, many don't. So, which ones need to be removed?

A new study reveals that a machine learning tool can help to identify which breast lesions, already classified as "high-risk," are likely to become cancerous. The researchers behind the study believe that the technology could eliminate unnecessary surgeries. Breast lesions are classified as high-risk after a biopsy reveals they have a higher chance of developing into cancer. Surgical removal is typically the recommended treatment option for these lesions due to the increased risk, even though many of these lesions do not pose an immediate threat. With "less immediate" cases, surgery may be deemed unnecessary and follow up imaging or other treatments may be found to be the preferred course of action -- but only if there is a reliable way of differentiating between the lesions.

US scientists are using artificial intelligence to predict whether breast lesions identified from a biopsy will turn out to cancerous. The machine learning system has been tested on 335 high-risk lesions, and correctly diagnosed 97% as malignant. It reduced the number of unnecessary surgeries by more than 30%, the scientists said. One breast cancer specialist said that the research was "useful". The machine learning system was trained on information about such lesions, the system looks for patterns among a range of data points, such as demographics, family history, biopsies and pathology reports.