AI helps predict when DCIS will progress to invasive breast cancer

Researchers have uncovered a new way to determine when ductal carcinoma in situ (DCIS) is most likely to progress to a more invasive cancer, according to new findings published in Breast Cancer Research.

The team used an advanced computer program to examine lumpectomy tissue samples from 62 different patients diagnosed with DCIS. This helped them focus on certain features of the tissue samples—tumor size and orientation, to be specific—that seemed to suggest a higher likelihood of DCIS progression. Those features were then combined with machine learning to establish detailed risk categories.

The researchers hope their work can limit the amount of radiation patients are exposed to when receiving care. It could also keep patients from undergoing the Oncotype DX genetic test when not necessary.

“Current testing places patients in high risk, low risk and indeterminate risk—but then treats those indeterminates with radiation, anyway,” Anant Madabhushi, department of biomedical engineering at Case Western Reserve University in Cleveland, said in a prepared statement. “They err on the side of caution, but we’re saying that it appears that it should go the other way—the middle should be classified with the lower risk.”

“This could be a tool for determining who really needs the radiation, or who needs the gene test, which is also very expensive,” lead author Haojia Li, department of biomedical engineering at Case Western Reserve University, said in the same statement.