A new study
has introduced a promising blood test that health professionals may soon use to
accurately detect brain cancer.
Dr. Matthew J. Baker, a reader in the Department of Pure and
Applied Chemistry at the University of Strathclyde in Glasgow, United Kingdom,
is the lead author of the new research.
He and his colleagues have now published their findings in the
journal Nature Communications.
Of the study, Dr. Baker says, "This is the first
publication of data from our clinical feasibility study, and it is the first
demonstration that our blood test works in the clinic."
Although it is quite rare, brain cancer often has a poor outlook.
According to the National Cancer Institute, around 0.6% of
people will develop brain cancer or another cancer of the nervous system in
their lifetime.
However, the 5 year survival rate for those who do receive such
a diagnosis is less than 33%.
Largely, the poor outlook is due to the fact that brain tumors
have very nonspecific symptoms, which makes them more difficult to distinguish
from other conditions.
Study co-author Dr. Paul Brennan — a senior clinical lecturer
and consultant neurosurgeon at the University of Edinburgh in the U.K. —
explains, "Diagnosing brain tumors is difficult, leading to delays and
frustration for lots of [people]."
"The problem is that symptoms of brain tumor are quite
nonspecific, such as headache, or memory problems. It can
be difficult for doctors to tell which people are most likely to have a brain
tumor," he adds.
The lack of cost effective tests that can help doctors triage
people with brain tumors in primary care also means that it takes longer to
accurately diagnose brain cancer. This ultimately results in a poorer outlook.
The team's new blood test brings much needed hope in this
regard. Dr. Baker and colleagues used infrared light to create a
"bio-signature" of people's blood samples and applied artificial
intelligence to scan for signs of cancer.
The test correctly identified brain cancer in a cohort of 104
people 87% of the time.
A more rapid means of diagnosis
As the researchers explain in their paper, they used a technique
called attenuated total reflection-Fourier transform infrared (ATR-FTIR)
spectroscopy and coupled it with machine learning technology to detect brain
cancer.
The authors explain that the technique is "a simple, label
free, noninvasive, nondestructive" way of analyzing the biochemical
profile of a blood sample without requiring extensive preparation of the
sample.
The ATR-FTIR technique allowed the researchers to work out a
biochemical "fingerprint" of brain cancer.
Dr. Baker and team trained a machine learning algorithm to use
these biochemical fingerprints to diagnose brain cancer in a retrospective
cohort of 724 people. This cohort included people with primary and secondary
cancers as well as control participants without cancer.
They then used the algorithm to predict brain cancer cases in a
sample of 104 participants. Of these, 12 people had cancer, including four
cases of glioblastoma. This is one of the most aggressive forms of brain tumor.
The findings revealed a sensitivity of 83.3% and a specificity
of 87% for the blood test. "With this new test, we have shown that we can
help doctors quickly identify which [people] with these nonspecific symptoms
should be prioritized for urgent brain imaging," says Dr. Brennan.
"This,"
he adds, "means a more rapid diagnosis for people with a brain tumor, and
quicker access to treatment."
Hayley Smith — an ambassador for the Brain Tumor Charity in
Hampshire, U.K. — adds that it is "very encouraging to hear that this
blood test can lead to a quicker diagnosis for brain cancer."
"This kind of test
will be vital to patients, helping people to get the correct diagnosis quicker,
which ultimately will help people to get the urgent medical care that they
need."
Hayley Smith
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