AI-Powered Blood Test Achieves 98% Accuracy in Early Breast Cancer Detection

A groundbreaking AI-powered blood test is revolutionizing early breast cancer detection. By combining laser analysis with artificial intelligence, this precise, non-invasive screening method identifies breast cancer at its earliest stage. Researchers are now aiming to expand this innovative technique to detect other types of cancer, potentially transforming cancer screening and improving patient outcomes worldwide.

Non-Invasive Technique Detects Changes in Stage 1a Breast Cancer

Researchers from the University of Edinburgh have developed a groundbreaking screening method that combines laser analysis with AI to detect breast cancer at its earliest stage. This fast, non-invasive technique identifies subtle bloodstream changes during the initial phase, known as stage 1a. As it currently stands, these changes are undetectable with existing tests. The team believes this method could enhance early detection and monitoring, and also see potential for screening multiple cancer types.

How the Test Works: Raman Spectroscopy and Machine Learning Combined

Standard breast cancer tests include physical exams, x-rays, ultrasound scans, or biopsies. Current early detection strategies screen people based on age and several risk factors such as family history, genetic predispositions, and lifestyle habits. This newly developed method by the research team detects breast cancer at its earliest stage. According to the team, similar methods have been tested for other cancers, but they could only detect the disease at stage two. 

Here, they enhanced a laser analysis technique called Raman spectroscopy and integrated it with machine learning, a type of AI. Essentially, researchers shine a laser beam into blood plasma taken from patients. The light’s properties, after interacting with the blood, are analyzed using a spectrometer to reveal tiny changes in the chemical makeup of cells and tissues. These changes are early indicators of disease. A machine learning algorithm then interprets the results, identifying similar features and helping to classify samples.

Pilot Study Results Highlight Potential Impact on Cancer Screening: Early Diagnosis Key to Long-Term Survival

In a pilot study involving 12 breast cancer patients and 12 healthy controls, the technique was 98% effective at identifying breast cancer at stage 1a. The test also distinguished between the four main subtypes of breast cancer with over 90% accuracy. According to the team, this could allow patients to receive more effective and personalized treatment.

Dr. Andy Downes, of the University of Edinburgh’s School of Engineering, who led the study, said: “Most deaths from cancer occur following a late-stage diagnosis after symptoms become apparent, so a future screening test for multiple cancer types could find these at a stage where they can be far more easily treated.”

Implementing this AI-powered blood test as a screening method could help identify more people in the earliest stages of breast cancer. This would improve the chances of successful treatment. The researchers believe that early diagnosis is key to long-term survival. Dr. Downes added: “Early diagnosis is key to long-term survival, and we finally have the technology required. We just need to apply it to other cancer types and build up a database, before this can be used as a multi-cancer test.”

Expanding Potential to Improve Patient Outcomes Not Just In Breast Cancer, But Across Multiple Cancers

The study, published in the Journal of Biophotonics, involved researchers from several renowned institutions. These included the University of Aberdeen, the Rhine-Waal University of Applied Sciences, and the Graduate School for Applied Research in North Rhine-Westphalia. Blood samples were generously provided by the Northern Ireland Biobank and Breast Cancer Now Tissue Bank. Moreover, Dr. Downes emphasized the critical importance of early diagnosis and highlighted the potential impact of this technology.

Author

Bernice Lottering