Researchers harness machine learning to predict breast cancer

A Dartmouth research team is harnessing machine learning technology to predict malignant breast cancer lesions. Saeed Hassanpour, assistant professor of biomedical data science and epidemology at the Geisel School of Medicine, and his team are focused on developing this technology to predict the possibility that a breast lesion found during medical examinations is or will become cancerous. Hassanpour said that breast cancer screenings are widely used, but can induce a false positive, which put women in danger of overdiagnosis and overtreatment. He explained that typically, if a lesion is found after a mammography, doctors perform a core needle biopsy on the patient. If a marker for high risk breast cancer incidences, known as atypical ductal hyperplasia, is found, surgery is performed to determine whether the lesion is malignant or benign, according to Hassanpour.