Machine learning model may save women from unnecessary breast surgery
Researchers have created a machine learning model that identified 98 percent of malignant atypical ductal hyperplasia (ADH) lesions prior to surgery, according to a single-center study published in JCO Clinical Cancer Informatics. The approach saved 16 percent of women from unnecessary surgery.
Lead researcher Saeed Hassanpour, PhD, of Dartmouth University in Hanover, New Hampshire and colleagues analyzed 128 lesions from 124 women with core needle biopsy-confirmed ADH who also underwent surgery.
Of the six machine learning models created by the team, the top performers included gradient-boosting trees, which calculated whether ADH would progress to cancer with 78 percent accuracy, and a random forest model that did so with 77 percent accuracy. According to the team, the random forest model would have accurately diagnosed 98 percent of malignant ADH cases via surgical biopsies and spared 16 percent of women from unnecessary surgeries on benign lesions.
"Our model can potentially help patients and clinicians choose an alternative management approach in low-risk cases,” Hassanpour said in a prepared statement. "In the era of personalized medicine, such models can be desirable for patients who value a shared decision-making approach with the ability to choose between surgical excision for certainty versus surveillance to avoid cost, stress and potential side effects in women at low risk for upgrade of ADH to cancer."
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Matt joined Chicago’s TriMed team in 2018 covering all areas of health imaging after two years reporting on the hospital field. He holds a bachelor’s in English from UIC, and enjoys a good cup of coffee and an interesting documentary.