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.
Edinburgh researchers 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.
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 trialled to screen for other types of cancer, but the earliest they could detect disease was at stage two, the team says.
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 trialled to screen for other types of cancer, but the earliest they could detect disease was at stage two, the team says.
In the pilot study involving 12 samples from breast cancer patients and 12 healthy controls, the technique was 98 per cent 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 per cent, which could enable patients to receive more effective, personalised treatment.
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 aims to expand the work to involve more participants and include tests for early forms of other cancer types.
Dr Andy Downes from the School of Engineering 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. 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.”
