Summary: Researchers at the University of Edinburgh have developed a groundbreaking laser analysis and AI method capable of detecting stage 1a breast cancer with 98% accuracy, offering a non-invasive, highly effective screening option.
Takeaways:
- Breakthrough in Early Detection: The method uses Raman spectroscopy and AI to identify subtle changes in blood plasma, detecting stage 1a breast cancer earlier than traditional tests.
- High Accuracy and Precision: In pilot studies, the technique achieved 98% accuracy for detecting stage 1a breast cancer and 90% accuracy in identifying the four main breast cancer subtypes, enabling personalized treatment approaches.
- Multi-Cancer Screening Potential: With further research and expanded databases, this method could be adapted for early detection of multiple cancer types, improving treatment success rates and survival outcomes.
A new screening method that combines laser analysis with a type of AI is the first of its kind to identify patients in the earliest stage of breast cancer, a study suggests.
Non-Invasive Cancer Screening Method
The fast, non-invasive technique reveals subtle changes in the bloodstream that occur during the initial phases of the disease, known as stage 1a, which are not detectable with existing tests, the team says.
Researchers at the University of Edinburgh say their new method could improve early detection and monitoring of the disease and pave the way for a screening test for multiple forms of cancer.
Better than Traditional Diagnostic Techniques
Standard tests for breast cancer can include a physical examination, x-ray or ultrasound scans or analysis of a sample of breast tissue, known as a biopsy. Existing early detection strategies rely upon screening people based on their age or if they are in at-risk groups.
Using the new method, researchers were able to spot breast cancer at the earliest stage by optimising a laser analysis technique—known as Raman spectroscopy—and combining it with machine learning, a form of AI.
Similar approaches have been trialed to screen for other types of cancer, but the earliest they could detect disease was at stage two, the team says.
Harnessing Laser Analysis and Machine Learning
The new technique works by first shining a laser beam into blood plasma taken from patients. The properties of the light after it interacts with the blood are then analyzed using a device called a spectrometer to reveal tiny changes in the chemical make-up of cells and tissues, which are early indicators of disease.
A machine learning algorithm is then used to interpret the results, identifying similar features and helping to classify samples.
In the pilot study involving 12 samples from breast cancer patients and 12 healthy controls, the technique was 98% effective at identifying breast cancer at stage 1a.
The test could also distinguish between each of the four main subtypes of breast cancer with an accuracy of more than 90%, which could enable patients to receive more effective, personalized treatment, the team says.
Implementing this as a screening test would help identify more people in the earliest stages of breast cancer and improve the chances of treatment being successful, the team says. They aim to expand the work to involve more participants and include tests for early forms of other cancer types.
Further Reading
The study is published in the Journal of Biophotonics. Blood samples used in the study were provided by the Northern Ireland Biobank and Breast Cancer Now Tissue Bank. It also involved researchers from the University of Aberdeen, the Rhine-Waal University of Applied Sciences and the Graduate School for Applied Research in North Rhine-Westphalia.
“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,” says Andy Downes, PhD, of the University of Edinburgh’s School of Engineering, who led the study. “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.”
