Machine Learning Model Predicts Risk of Upgrade to Breast CA

Scientists discover a new way of diagnosing breast cancer — using artificial intelligence

Scientists discover a new way of diagnosing breast cancer — using artificial intelligence

Suspicions lesions that are discovered through a mammogram will subsequently be tested with a needle biopsy.

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.

"This work highlights an example of using cutting-edge machine learning technology to avoid unnecessary surgery", Dr. Marc Kohli, director of clinical informatics in the Department of Radiology and Biomedical Imaging at the University of California at San Francisco, said in a statement. "It may be reasonable for some patients to have their lesions followed with imaging rather than surgically excised".

Machine learning could drastically improve medical treatment for breast lesions.

Two authors disclosed having a patent in process with Massachusetts General Hospital and the Massachusetts Institute of Technology, and one author disclosed ties to GE Healthcare. "Most institutions recommend surgical excision for high-risk lesions such as atypical ductal hyperplasia, for which the risk of upgrade to cancer is about 20 percent".

"For other types of high-risk lesions, the risk of upgrade varies quite a bit in the literature, and patient management, including the decision about whether to remove or survey the lesion, varies across practices", Ms. Bahl said.

High-risk breast lesions are biopsy-diagnosed ones that carry an increased risk of developing into cancer. Machine learning allows an artificial intelligence (AI) system to learn from its past experiences and improve its performance as a result, similar to the way in which humans learn.

The research team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital and Harvard Medical School developed a machine learning model that was trained on 600 existing high risk lesions, incorporating variables as broad as family history, demographics and past biopsies. The model correctly predicted 37 of the 38 lesions that were upgraded to cancer, identifying the terms "severely" and "severely atypical" in the text of the pathology reports as associated with a greater risk of upgrade to cancer.

Researchers studied the use of a machine learning tool to identify high-risk lesions that are at low risk for upgrade to cancer. The team found that, had the model been used, it would have helped to prevent almost one-third of the surgeries conducted on benign lesions.

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