British oncologists have developed an AI model to help predict whether advanced breast cancer will spread based on changes in a patient’s lymph nodes.
The research content is published on thursday This paper was published in the Journal of Pathology by Breast Cancer Now and was funded by scientists at King’s College, London.
Secondary or “metastatic breast cancer” refers to when breast cancer cells spread to other parts of the body. Even if it is treatable, it cannot be cured.
Researchers involved in last week’s study hope to use AI to analyze the immune response of lymph nodes in women with triple-negative breast cancer to better measure the likelihood of disease spreading.
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Lymph nodes are clumps of tissue throughout the body that are important for helping the body fight infections and diseases. Once breast cancer cells start spreading, patients usually need more intensive treatment.
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Still, researchers believe it’s possible to predict whether cancer cells will spread based on the immune response.
Dr. Anita Grigoriadis, who led the study, said the team would translate the findings obtained under the microscope into a “deep learning framework to create AI models that could help doctors treat and care for their patients. It gave doctors another tool.” It is a weapon that helps prevent secondary breast cancer. ”
The researchers tested the AI model on more than 5,000 lymph nodes donated to the biobank from about 350 patients.
“By demonstrating that nodal changes can predict whether triple-negative breast cancer spreads, we are increasing our knowledge that the immune response may play an important role in understanding patient prognosis.” said Grigoriadis.
The team plans to test the model further at centers across Europe.
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“Moving from evaluating tissue on slides under a microscope to using computers in the medical setting. [National Health Service] “We hope to take advantage of this shift to develop AI-powered software based on models that pathologists can use to benefit women with this difficult-to-treat breast cancer,” said Grigoriadis. .