NEW DELHI — New
machine learning-based screening method is 98 pc effective in detecting the
earliest sign of breast cancer, according to a study.
Developed by researchers at the University of Edinburgh the
fast, non-invasive technique combines laser analysis with machine learning. It
is the first of its kind to identify patients in the earliest stage of breast
cancer, they said, noting that it may pave the way for a screening test for
multiple forms of cancer.
The technique can pick up subtle changes that occur in the
bloodstream during the initial phases of the disease -- known as stage 1a --
which goes undetected by existing tests.
Physical examination, X-ray or ultrasound scans, or analysis
of a sample of breast tissue, known as a biopsy are standard tests currently
available for breast cancer. These rely upon screening people based on their
age or if they are in at-risk groups.
The pilot study, published in the Journal of Biophotonics,
involved 12 samples from breast cancer patients and 12 healthy controls. In the
study, the team optimised a laser analysis technique -- known as Raman
spectroscopy -- and combined it with machine learning.
The team could spot breast cancer at stage 1a with 98 per
cent effectiveness.
It first shines a laser beam into blood plasma taken from
patients. Using a spectrometer device, the team analysed the properties of the
light after it interacted with the blood. The spectrometer then revealed tiny
changes in the chemical makeup of cells and tissues -- early indicators of
disease.
Using the machine learning algorithm, physicians can
interpret the results. Using the novel approach, the team could also
distinguish between each of the four main breast cancer subtypes with an
accuracy of more than 90 per cent. The team said this enabled patients to
receive more effective, personalised treatment.
