Artificial intelligence can assess breast cancer better than doctors and could spare thousands of women unnecessary surgery, according to a new study.
Breast cancer is the most common cancer in the UK, with around 55,000 women and 400 men diagnosed every year.
In the US, where the study took place, around one in eight women will receive a diagnosis in their lifetimes.
During diagnosis, cancers are graded by pathologists to determine how abnormal the tissue is.
However, picking up on non-cancerous cells which can indicate whether the cancer will grow or recede can be difficult for the human eye.
But a team of scientists from Northwestern University, Illinois, have developed an AI that could prevent patients from undergoing unnecessary chemotherapy treatment, which can be unpleasant and harmful.
The authors behind the study, published in the journal Nature Medicine, also say the AI technology could be invaluable in helping doctors to assess cancers and predict individual outcomes.
Grading, which helps to determine what treatment a patient will receive, currently focuses solely on the appearance of cancer cells and has remained largely unchanged for decades.
However, many studies of breast cancer biology have shown that non-cancerous cells – including those from the immune system and non-cancerous cells which provide form and structure to the tissue – can play an important role in sustaining or inhibiting the growth and progress of a cancer.
The Northwestern researchers, led by study author Dr Lee Cooper, an associate professor of pathology at Northwestern, built an AI model to evaluate breast cancer tissue from digital images that evaluates the appearance both of cancerous and non-cancerous cells, and the interactions between them.
‘Our study demonstrates the importance of non-cancer components in determining a patient’s outcome,’ said Dr Cooper.
‘These patterns are challenging for a pathologist to evaluate as they can be difficult for the human eye to categorise reliably.
‘The AI model measures these patterns and presents information to the pathologist in a way that makes the AI decision-making process clear.’
The AI model analyses 26 different properties of a patient’s breast tissue to generate an overall score, as well as generating individual scores for cancer, immune and other cells to explain the overall result to the pathologist.
This information can subsequently be used by a patient’s care team in creating an individualised treatment plan.
Symptoms of breast cancer and how to check yourself
Look
- Look at your boobs, pecs or chest
- Look at the area from your armpit, across and beneath your boobs, pecs or chest, and up to your collarbone
Be aware of any changes in size, outline or shape and changes in skin such as puckering or dimpling.
Feel
- Feel each of your boobs, pecs or chest
- Feel the area from your armpit, across and beneath your boobs, pecs or chest, and up to your collarbone
Be aware of any changes in skin such as puckering or dimpling, or any lumps, bumps or skin thickening which are different from the opposite side.
Notice your nipples
- Look at each of your nipples
Be aware of any nipple discharge that’s not milky, any bleeding from the nipple, any rash or crusting on or around your nipple area that doesn’t heal easily and any change in the position of your nipple.
The new evaluation method could also provide breast cancer patients with a more accurate estimate of the risk associated with their disease, empowering them to make informed decisions about their treatment.
The researchers say it could also help to assess the response to treatment, allowing it to be escalated or reduced, depending on how the appearance of the tissue changes over time.
For instance, the tool could be used to recognise the effectiveness of a patient’s immune system in targeting their cancer during chemotherapy, which could be used to reduce the duration or intensity of chemotherapy.
To train their AI model, the researchers used hundreds of thousands of digital images of patient tissues by creating an international network of medical students and pathologists across several continents.
The study was conducted in collaboration with the American Cancer Society (ACS).
Next, Dr Cooper’s team will evaluate the model to validate it for future clinical use.
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MORE : Thousands of women could be saved from unnecessary breast cancer treatment
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