Experts have trained an AI model to identify breast cancer patients who could be spared from axillary surgery.
The project team at Lund University in Sweden created a model that analyses previously unutilised information in mammograms to accurately pinpoint individual risk of metastasis in the armpit.
A new study shows that the model indicates that just over 40% of today’s axillary surgery procedures could be avoided.
In breast cancer cases, axillary lymph nodes are examined to assess metastatic spread and thus the prognosis and choice of treatment.
A minor operation is performed, in which the first lymph nodes are identified and surgically removed for analysis. The procedure is minor but may cause pain, swelling, numbness and sometimes fluid collection.
The spread of cancer to the armpit affects approximately one in five breast cancer patients. The remaining 80% or so have no trace of cancer in the lymph nodes. In this case the procedure is purely diagnostic, with no therapeutic effect.
A considerable emphasis in research has been placed on developing non-invasive diagnostic methods that can determine lymph node status at an earlier stage than is currently today.
Lisa Rydén is responsible for the NILS (Non-Invasive Lymph node Staging) research project, a multiple-year research collaboration between the Faculty of Medicine and Faculty of Science at Lund University.
She said: ‘At present, a separate procedure is performed on the armpit – a sentinel lymph node biopsy – to determine the spread to the lymph nodes. Using NILS as the starting point and patient and tumour data as a basis, we would instead be able to make a more individualised risk assessment before the operation. If the risk is low, axillary surgery can be avoided after a dialogue with the patient. If the risk is high, we would plan for surgery. It would be a step towards more person-centred care in which each action has a clear benefit for that specific patient.’
In the study, researchers created an AI model that was trained to analyse mammograms. The mammograms are taken routinely in connection with breast cancer diagnostic work up, and thus entail no extra measures or costs. Images from 1,265 women in Skåne, diagnosed with breast cancer between 2009 and 2017, were used.
The common denominator for the study participants was that their breast cancer was at an early stage and that surgery was the first treatment. The study’s results have been published in the research journal NPJ Digital Medicine.
The AI model was trained to identify different types of information – from the whole mammogram, not just the part that showed the tumour. It is nbased of this complex information that the model can then calculate the risk of metastasis.
Daqu Zhang, doctoral student at the Faculty of Science, Lund University, said: ‘We developed our algorithm in three steps. Firstly, the AI model went through tens of thousands of mammograms to learn their basic structure, such as edges, texture and shapes. The AI model was then trained to find specific clues for cancer, such as the boundaries of tumours. And finally it was given a “holistic mindset” by including other important patient information, like age and tumour type, in order to more accurately predict the risk of metastasis.’
The AI model was used to classify the lymph nodes as disease-free or not, and the researchers showed that sentinel lymph node biopsy could have been avoided in 41.7% of the cases.
Sweden stands out internationally for its frequent and public mammography screening.
Around 67% of Swedish breast cancer cases are detected in mammography screening among women aged between 40 and 74. Examination invitations are sent out at 18-to 24-month intervals, depending on the woman’s age.
Rydén hopes that the AI model’s built-in calculation algorithms can be utilised during the mammography examination stage to assess the risk of lymph node metastasis. That treatment can then be promptly designed based on this risk.
Photo - Members of the NILS project, from left: Looket Dihge, Faculty of Medicine, Lund University; Patrik Edén, Faculty of Science, Lund University; Lisa Rydén, Faculty of Medicine, Lund University; and Daqu Zhang, Faculty of Science, Lund University. Photo: Ingemar Hultquist
