Reviewed by Lexie CornerMar 21 2024
An artificial intelligence (AI) tool created by a group of international researchers can identify breast cancer patients who may be more susceptible to side effects following surgery and radiation treatment.
Dr. Tim Rattay. Image Credit: Dr. Tim Rattay
Dr. Tim Rattay announced at the 14th European Breast Cancer Conference (EBCC14) in Milan that the tool will undergo testing in a clinical trial. The trial is set to commence recruitment in the final quarter of this year across three countries: France, The Netherlands, and the UK.
It is an explainable AI tool, which means that it shows the reasoning behind its decision-making. This makes it easier for doctors to make decisions and provide data-backed explanations to their patients.
Dr. Tim Rattay, Consultant Breast Surgeon and Associate Professor, Leicester Cancer Research Centre, University of Leicester
While some of the risk factors for side effects are already known, the PRE-ACT project (Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification) aims to provide physicians and patients with easily understandable explanations along with more accurate predictions for each patient.
Thankfully, long-term survival rates from breast cancer continue to increase, but for some patients, this means having to live with the side effects of their treatment. These include skin changes, scarring, lymphoedema, which is a painful swelling of the arm, and even heart damage from radiation treatment. That is why 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 assist doctors and patients in choosing options for radiation treatment and reduce side effects for all patients.
Dr. Tim Rattay, Consultant Breast Surgeon and Associate Professor, Leicester Cancer Research Centre, University of Leicester
The researchers from six European nations trained various machine learning algorithms to predict arm swelling up to three years following surgery and radiotherapy using data from three European and French datasets (REQUITE, Hypo-G, and CANTO) on 6,361 patients with breast cancer.
The final, best-performing model makes predictions using 32 different patient and treatment features, including whether or not patients had chemotherapy, whether sentinel lymph node biopsy under the armpit was carried out, and the type of radiotherapy given.
Dr. Guido Bologna, Co-Investigator and Associate Professor, University of Applied Sciences and Arts
In the three datasets, 6 % of patients had significant lymphoedema. In 81.6 % of cases, the AI tool accurately predicted lymphoedema, while in 72.9 % of cases, it correctly identified people who would not acquire it. The model's overall prediction accuracy was 73.4 %.
Dr. Rattay said, “Patients identified at higher risk of arm swelling could 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. Clinicians may also use this information to discuss options for lymph node irradiation in patients, where its benefit may be fairly borderline. We will test the effect of the prediction model on clinician and patient behavior and use of the prophylactic arm sleeve in the proposed clinical trial.”
The researchers will integrate the present AI model into software that may give physicians and patients assessments and forecasts. Later this year, when the PRE-ACT-01 clinical trial begins, this will be put to the test. Additionally, the program is being developed further to forecast other adverse effects, like harm to the skin and heart.
Although they will not be utilized to generate predictions in the PRE-ACT study, the researchers will gather information on genetic markers and imaging data as part of the experiment to increase the precision of the AI tools.
Dr. Rattay said, “We hope to recruit approximately 780 patients by early 2026, with a follow-up period of two years.”
The 14th European Breast Cancer Conference's Chair, Professor Michail Ignatiadis of the Institut Jules Bordet in Brussels, Belgium, was not involved in the research.
Ignatiadis said, “The PRE-ACT project is a nice example of how international collaboration between researchers is harnessing the potential of AI to make it easier for clinicians to predict and try to prevent arm lymphoedema and to explain the options to their patients in an understandable way.”