A new method developed by Edinburgh researchers combines laser analysis with a type of AI, which could improve early detection and monitoring of bresat cancer 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 at the University of Edinburgh were able to spot breast cancer at 1a - the earliest stage of the disease - by optimising a laser analysis technique known as Raman spectroscopy and combining it with machine learning, a form of AI.  

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 analysed 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 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.