A Dartmouth research team is harnessing technology to predict malignant breast cancer lesions.
A Dartmouth research team is harnessing 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.
“Seventy to 80 percent of women didn’t need this surgery,” Hassanpour explained. “Only 20 to 30 percent of patients are [found to have cancerous lesions].”
Following this discovery of overdiagnosis and treatment, Hassanpour and his colleagues searched for less-invasive alternatives for women seeking diagnoses.
“We thought it would be good to introduce a personalized decision-making approach” he added.
Hassanpour said that he and his team want to help women who do not need surgery avoid the distress, costs and potential side-effects of an unnecessary operation.
“If patients demonstrate they have a lower risk of cancer, they can decide if they want to go through with the surgery or opt for an alternative, less invasive surveillance” Hassanpour said.
Hassanpour said his lab is comprised of students from the quantitative and deductive sciences PhD and Masters program and the computer science PhD, Masters and undergraduate programs. Other members include research scientists and post-doctoral fellows with backgrounds in computer and data science.
“My lab works in developing new and methods to distill health-related insights from bio-medical data” Hassanpour explained. “This is mostly unstructured data like medical records or images.”[…]