April 16, 2024

Doctors have developed an artificial intelligence tool that can predict which breast cancer patients are more at risk for side effects after treatment.

Worldwide, 2 million women are diagnosed with the disease each year, which is the most common cancer in women in most countries.

Greater awareness, earlier detection and a wider range of treatment options have improved survival rates in recent years, but many patients will often experience debilitating side effects after treatment.

An international team of medics, scientists and researchers has designed an AI tool that can indicate how likely a patient is to experience problems after surgery and radiotherapy. The technology, which is being tested in the UK, France and the Netherlands, could help patients get more personalized care.

“Thankfully, long-term survival rates from breast cancer continue to increase, but for some patients this means they have to live with the side effects of their treatment,” said Dr Tim Rattay, a consultant breast surgeon and associate professor at the University of Leicester. “These include skin changes, scarring, lymphoedema, which is a painful swelling of the arm, and even heart damage from radiation treatment.

“Therefore, we are developing an AI tool to inform doctors and patients about the risk of chronic arm swelling after surgery and radiotherapy for breast cancer. We hope this will help doctors and patients choose radiation treatment options and reduce side effects for all patients.”

The AI ​​tool was trained to predict lymphedema up to three years after surgery and radiotherapy using data from 6,361 breast cancer patients. Patients who are at higher risk of arm swelling may be offered alternative treatments or additional support during and after treatments.

Dr Guido Bologna, associate professor at the University of Applied Sciences and Arts of Western Switzerland in Geneva and co-investigator of the project, said: “The final, best-performing model makes predictions using 32 different patient and treatment characteristics, including or whether or not patients have chemotherapy, or sentinel lymph node biopsy under the armpit was performed, and the type of radiotherapy given.”

The AI ​​tool correctly predicted lymphoedema in an average of 81.6% of cases and correctly identified patients who would not develop it in an average of 72.9% of cases. The overall predictive accuracy of the model was 73.4%.

“Patients who are at higher risk of arm swelling may be offered additional supportive measures, such as wearing an arm compression sleeve during treatment, which has been shown to reduce arm swelling in the long term,” Rattay said. “Clinicians can also use this information to discuss options for lymph node radiation in patients where its benefit may be quite borderline.”

Talk to the European Chest Cancer conference in Milan, Rattay said the technology is “an explainable AI tool, meaning it shows the reasoning behind its decision-making.

“This not only makes it easier for doctors to make decisions, but also to provide data-backed explanations to their patients,” he added.

The research team hopes to enroll 780 patients as part of a clinical trial called the Pre-Act project, which will be followed up for a period of two years. They are also developing the tool to predict other side effects, including skin and heart damage.

Dr Simon Vincent, director of research, support and advocacy at Breast Cancer Now, said ways to improve treatments were urgently needed. “This exciting project will investigate whether the use of AI can enable people with breast cancer to receive more personalized care and support that helps reduce side effects, such as chronic arm swelling, after surgery and radiotherapy.

“This research is in its early stages and more evidence is needed before we can consider whether or not the AI ​​tool can be used in medical settings, and we look forward to seeing results from the trial.”

In other developments at the conference, researchers from Italy found that using combined positron emission tomography-magnetic resonance imaging (PET-MRI) scans allowed doctors to see that a breast cancer patient’s tumor had started to spread. This meant that they could benefit from alternative treatment, such as chemotherapy or another type of surgery.

Meanwhile, researchers from the Netherlands said young breast cancer patients who received a low-dose boost of radiotherapy to where their tumor was removed, in addition to whole-breast radiation, remained free of local recurrence after 10 years.

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