The research team recently compared three different risk assessment approaches. The first model relied on traditional risk factors, the second on deep learning that used the mammogram alone, and the third on a hybrid approach that incorporated both the mammogram and traditional risk factors into the deep learning model.
Researchers have developed a new tool with advanced artificial intelligence (AI) methods to predict a woman's future risk of breast cancer.
For the study, the researchers used almost 90,000 full-resolution screening mammograms from about 40,000 women to train, validate and test the deep learning model.
"There's much more information in a mammogram than just the four categories of breast density, "said study lead author Adam Yala from the Massachusetts Institute of Technology (MIT) in the US.
"By using the deep learning model, we learn subtle cues that are indicative of future cancer," Yala added.
The research team recently compared three different risk assessment approaches. The first model relied on traditional risk factors, the second on deep learning that used the mammogram alone, and the third on a hybrid approach that incorporated both the mammogram and traditional risk factors into the deep learning model.
The deep learning models yielded substantially improved risk discrimination over the Tyrer-Cuzick model, a current clinical standard that uses breast density in factoring risk.
When comparing the hybrid deep learning model against breast density, the researchers found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk.
The advantages held across different subgroups of women, said the study published in the journal Radiology.
"Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories," said Regina Barzilay, Professor at MIT.
