One of the areas that will ­benefit most from AI is ­medical research, and that can be seen in a stunning King’s College study.

What could be more life-changing than an AI tool that can predict whether an aggressive type of breast cancer will spread or not, based on changes in a patient’s lymph nodes?

Lymph nodes are pea-sized lumps of tissue found throughout the body that help fight infection. Breast cancer cells typically first spread to lymph nodes in the armpit which are closest to the tumour.

By analysing immune responses in the lymph nodes of women with triple negative breast cancer, it’s possible to tell how likely the disease is to spread to other parts of the body.

When breast cancer cells spread from the primary cancer to other parts of the body, it’s called secondary or metastatic breast cancer, and although treatable, it can’t be cured. The team at King’s College London have developed an AI model to predict how likely a patient is to develop secondary breast cancer based on immune responses in the lymph nodes.

However, scientists discovered that even when breast cancer cells hadn’t spread to the lymph nodes, it was still possible to predict the ­likelihood of the cancer spreading from immune responses. Incredible.

“We’ve taken these findings from under the microscope and translated them into a deep-learning framework – to create an AI model to potentially help doctors treat and care for patients. This provides them with another tool in their arsenal for helping to prevent secondary breast cancer,” said Dr Anita Grigoriadis of King’s, who led the research.

The scientists tested their AI model on more than 5,000 lymph nodes donated by 345 patients and confirmed it could establish the ­likelihood of breast cancer spreading.

Around 15% of breast cancers are triple negative, which is more likely than most other breast cancers to return or spread during the first years following treatment.

Dr Grigoriadis added: “By ­demonstrating that lymph node changes can predict if triple negative breast cancer will spread, we’ve built on our growing knowledge of the important role that immune
response can play in understanding a patient’s prognosis.

“We’re planning to test the model further at centres across Europe to make it even more robust and precise. The transition from assessing tissue on glass slides under a microscope to using computers is gathering pace.

“We want to leverage this change to develop AI-powered software based on our model for pathologists to use to benefit women with this hard-to-treat breast cancer.”