AI tool can predict breast cancer progression

An AI tool has identified microscopic breast cancer patterns that could help medical professionals better forecast disease progression.

The tool, called CenSegNet, was developed to analyse hundreds of thousands of cells in tumour samples and detect abnormalities in structures known as centrosomes.

Centrosomes are small structures inside cells that ensure DNA is divided equally during cell replication. Researchers say abnormalities in these structures have been considered a hallmark of cancer for more than a century.

In cancerous tissue, centrosomes can replicate excessively, driving the progression of the disease.

Scientists at the University of Southampton used the system to study tissue from 127 breast cancer patients being treated at University Hospital Southampton.

More than 330,000 centrosomes were analysed, revealing two distinct abnormalities that had previously been considered part of the same process.

One involved cells developing too many centrosomes, while the other involved centrosomes becoming abnormally enlarged.

Researchers found the two defects behaved independently and could occur in different areas of the same tumour.

Dr Salah Elias, of the University of Southampton’s school of biological sciences and institute for life sciences, said: “For more than a century, centrosome abnormalities have been recognised as a hallmark of cancer, but studying them in patient tissues has been extremely challenging.

“CenSegNet allows us to analyse these defects at single-cell resolution across entire tumours and uncover patterns that were previously impossible to see.

“Rather than viewing centrosome abnormalities as a single phenomenon, our study shows that they have distinct biological states with different spatial distributions and clinical associations.”

The platform also helped uncover a link between different centrosome abnormalities and features of cancer.

Tumours with high levels of enlarged centrosomes were more aggressive, while patients whose cells had lower levels had a better chance of survival.

Dr Elias said: “Specific combinations of defects may influence how a tumour grows, invades surrounding tissues and responds to treatment.

This opens the door to developing new biomarkers and, ultimately, more personalised treatment strategies.”

Researchers hope AI could eventually be used to track disease by analysing the behaviour of cell structures.

The team also plans to combine CenSegNet with more data to explore whether it could help guide treatment decisions.