QF researcher underlines accuracy of AI in breast cancer detection through images

Breast cancer is known to be the leading cause of cancer-related deaths among women worldwide, and compared to the countries in the Middle East, Qatar has one of the highest breast cancer incidence and mortality rates. Regular screening and early detection are crucial, with the American Cancer Society stating that when breast cancer is detected early, and is in the localized stage, the five-year relative survival rate is 99 percent.

There are numerous breast diagnostic approaches, such as mammography, magnetic resonance imaging (MRI), ultrasound among others, and the use of Artificial Intelligence (AI) in these diagnostic technologies is becoming increasingly popular.

Currently, digital mammography is used as the standard method for early breast cancer detection, but it appears to have its limitations, and AI is coming to its rescue. AI models are being developed and used to predict breast cancer in mammography scans with more accuracy than radiologists, thereby reducing false positives and false negatives.

“When using the naked eye to define abnormalities in image data or while analyzing tissue, one could go wrong in the analysis. However, with artificial intelligence, classification of abnormal or normal tissue is more accurate,” says Dr. Halima Bensmail, Principal Scientist and Associate Professor at Qatar Computing Research Institute (QCRI), part of Qatar Foundation’s (QF’s) Hamad Bin Khalifa University (HBKU).

She adds: “Due to the extensive variation from patient to patient data, traditional learning methods are not reliable, and machine learning has evolved over the last few years by its ability to sift through complex and big data to be able to detect abnormalities.”

In the area of digital imaging – Qatar’s mammography image quality is deemed adequate, as per a study titled Breast Cancer Detection in Qatar: Evaluation of Mammography Image Quality Using A Standardized Assessment Tool, funded by QF's Qatar National Research Fund. But the study also notes that as the country develops additional capacity and awareness for mammography screening, it will be important to continuously monitor image quality.

“People will always ask – what is the accuracy of your prediction, or what is more important when designing a machine learning model: model performance or model accuracy. This answer depends on the application and the field. But so far, we don't have any machine learning algorithm or artificial intelligence model that gives us 100 percent accuracy of the prediction,” says Dr. Bensmail.

So, how does AI work when it comes to detecting breast cancer or any other type of cancer?

A lot of AI is built on machine learning. In machine learning, scientists train the system to learn something very specific, such as bad breast tissue versus good breast tissue through images. By training the system with massive amounts of data, it learns to differentiate between bad tissue and good tissue. Over time, the algorithm learns to predict with great accuracy.

AI algorithms such as deep learning and neural network-based algorithm offer extremely good results in breast cancer detection – they provide 90 to 97 percent accuracy of image data, such as in mammograms. However, when enough data isn’t available, machine learning or AI models cannot be effectively built – and this is a challenge in the region, according to Dr. Bensmail.

She says: “In the Arab world, breast cancer ranks as the most frequently diagnosed cancer overall, representing an estimated 17.7 to 19 percent of all new cancers in 2018 but there is some stigma among Arab women to get screened for breast cancer. You really have to motivate and encourage them, which is not always easy. And people also have concerns over privacy of data.”

With AI advancing rapidly, Dr. Bensmail predicts that within the next 10 years or so, AI will become even more common in clinical practice. She says predicting a disease, particularly classifying breast cancer in the radiology department, is something that is happening rapidly, specifically in the area of image data analysis. “There are a lot of institutes and groups of researchers working on the processing of image data in an accurate manner.”