The research team, led by Constance Lehman, MD, PhD, director of breast imaging at Mass General, published its findings in a paper online in the journal Radiology.
Researchers reviewed patients that had biopsy-proven high-risk breast lesions with at least two years of imaging follow-up to derive a machine learning model that can help identify lesions at low-risk for becoming cancerous. The model uses age and histologic results, as well as text features from the biopsy pathologic report to determine at-risk lesions.
The model identified 97 percent of lesions that were malignant and reduced unnecessary surgery by 30 percent.
“This study provides proof of concept that a machine learning model can be applied to predict the risk of upgrade of HRLs to cancer,” the authors wrote. “Use of this model … could help guide clinical decision making with regard to surveillance versus surgical excision of HRLs.”
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