The new research from the University of Copenhagen has found that AI could predict the risk of breast cancer.
Breast cancer has become more common. In 2022, the disease claimed 670,000 lives worldwide. The new study revealed that the AI can detect the damaged cells and improve the treatment by predicting the risk of breast cancer.
According to the study published in ‘The Lancet Digital Health’, AI technology works far better than Gail model, the current gold standard for breast cancer risk assessment. The researchers deployed deep learning AI technology developed at the University of Copenhagen to analyze mammary tissue biopsies from donors to look for the signs of damaged cells caused by cellular senescence, which is the indicator of cancer risk.
Associate Professor Morten Scheibye-Knudsen from the Department of Cellular and Molecular Medicine and senior author of the study, commented that, “The algorithm is a great leap forward in our ability to identify these cells. Millions of biopsies are taken every year, and this technology can help us better identify risks and give women better treatment.”
Senescent cells remain metabolically active but cease dividing. Previous study has shown that this senescent state can help suppress cancer development. However, senescent cells can also cause inflammation that leads to tumor development.
Indra Heckenbach Postdoc – Department of Cellular and Molecular Medicine
“We also found that if we combine two of our own models or one of our models with the Gail score, we get results that are far better at predicting risk of getting cancer. One model combination gave us an odds ratio of 4.70 and that is huge. It is significant if we can look at cells from an otherwise healthy biopsy sample and predict that the donor has almost five times the risk of developing cancer several years later. “
The researchers trained the AI technology on cells development in cell culture that were intentionally damaged to make them senescent. The AI technology was then used on the donor biopsies to identify senescent cells.
Indra Heckenbach stated that, “We sometimes refer to them as zombie cells because they have lost some of their function, but they are not quite dead. They are associated with cancer development, so we developed and trained the algorithm to predict cell senescence. Specifically, our algorithm looks at how the cell nuclei are shaped, because the nuclei become more irregular when the cells are senescent”
It will take several years for the technology to be available at the clinic, but then it can be applied worldwide, as it only requires standard tissue sample images to do the analysis.
“We will be able use this information to stratify patients by risk and improve treatment and screening protocols. Doctors can keep a closer eye on high-risk individuals, they can undergo more frequent mammograms and biopsies, and we can potentially catch cancer earlier. At the same time, we can reduce the burden for low-risk individuals, e.g. by taking biopsies less frequently, ” added Morten Scheibye-Knudsen.
