AI may be able to help detect cancer in the blood quicker.
- Researchers
have designed a simple, cheap experimental test for diagnosing cancer.
- The
test requires just a tiny dried blood spot.
- The
researchers found that it has a sensitivity of 82–100% and takes just a
few minutes.
- This
approach may be particularly useful for people in low-income countries.
Scientists in China have
created a dried blood spot test to diagnose cancer. In the new study, they
focused on pancreatic, gastric, and colorectal cancer.
The system, which uses a
form of artificial intelligence (AI) called machine learning, is significantly
quicker and more cost-effective than current whole blood tests and other
diagnostic techniques.
According to their recent
paper in
The rise of
AI in medicine
Today,
virtually everything is powered by AI, for better or worse. But while AI might
be stealing people’s jobs and creating terrible “art,” its powers can also be
used for good.
Medical researchers are
busy wielding the cutting edge of AI to help us understand and manage disease.
One part of this voyage of
discovery is identifying innovative ways to diagnose medical conditions. This
is important work — catching diseases earlier generally leads to better
outcomes.
Because some cancers are
difficult to diagnose and lack reliable blood markers, some experts are
investigating whether AI can help.
Currently, accurate
diagnosis often requires expensive facilities, equipment, and transport. For
instance, whole blood needs temperature-controlled storage in transit, which
comes at a price.
As the authors of a new
paper write, “Cost-effectiveness is key in disease screening.”
These costs are an
additional burden for developing countries and poorer regions, where many
cancer cases are missed due to a lack of access to healthcare. Because of this,
some experts believe that by 2030,
Using blood
spot tests to detect cancer
Some
diseases can already be
However, the most common
diagnostic markers for cancer, such as microRNAs and proteins, are more easily
disrupted during drying. Also, the small amount of blood harvested for a blood
spot test is generally insufficient for cancer diagnosis.
Medical News Today spoke with Dr. Joel Newman, a consultant hematologist and
clinical lead for pathology at Eastbourne District General Hospital, who was
not involved in the study.
He talked about the
difficulties of using blood spots to detect cancer:
“You have to find something
detectable in a tiny amount of blood, that can be reproducibly linked with a
cancer. What you don’t want to do is detect something that might be naturally
occurring and lead to unnecessary further investigations or worry.”
The recent study takes an
innovative approach. Rather than focusing on existing cancer markers, their
technology detects cancer-related metabolic changes. As the authors explain,
this is because “most metabolites remain stable on dried spots.”
They believe a
cost-effective, rapid AI-powered dry blood spot test for cancer may be a viable
option. Their experimental test relies on a technology called
nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI
MS).
More
reliable, quick, and safe than whole blood tests
Using
their experimental test, the researchers showed it could diagnose cancer using
dried blood spots with a sensitivity of 82–100%. This outperforms current whole
blood tests, which they say have a sensitivity of 50–80%.
As part of their research,
they exposed the blood spot tests to a range of temperatures and environmental
conditions. They found that the samples remained viable. In comparison, many
standard whole blood tests require very low temperatures to prevent spoiling.
Also, standard tests rely
on expensive, time-consuming pretreatment of samples, whereas the blood spot
test can be analyzed directly, saving time and money. Similarly, blood spot
tests require less physical space, making them easier and cheaper to transport.
This approach may also be
safer: The process of drying blood spots inactivates some harmful pathogens,
which remain active in whole blood.
Helping spot
missed diagnosis of cancer
As
part of their analysis, the authors assessed how many extra cases of cancer
they might pick up using their dried blood spot system if it was widely
implemented.
Currently, screening for
colorectal cancer relies primarily on colonoscopy, pancreatic cancer requires computed
tomography (CT scans), and gastric cancer is diagnosed using
gastroscopy. These are all expensive techniques that require skilled medical
staff.
In contrast, the authors
explain that their approach “can achieve a high level of diagnostic accuracy,
even when carried out by local health workers in resource-limited clinical
settings.”
They estimate that
undiagnosed cancer cases in underserved populations range from 34.56% to
84.30%.
However, if this new
approach to population-based cancer screening was implemented in rural China,
the authors estimate that rates of undiagnosed cases would fall from:
- 84.30% to
29.20% for colorectal cancer
- 34.56% to
9.30% for pancreatic cancer
- 77.57% to
57.22% for gastric cancer
MNT spoke
with Anton Bilchik, MD, Ph.D.,
surgical oncologist, chief of medicine, and director of the Gastrointestinal
and Hepatobiliary Program at Providence Saint John’s Cancer Institute in Santa
Monica, CA, who was not involved in the study.
We asked whether these
results were surprising:
“I was very surprised by
these findings. The reduced estimated percentages of undiagnosed cancer cases
[…] is astonishing, particularly in less developed areas.”
How much
would the blood test cost?
To
help avoid missed diagnoses, the blood spot tests would need to be rolled out
population-wide, meaning that cost is an important factor.
The authors provide one
example of how their technology can save money: An envelope with 100 filter
paper dried blood spot tests can be sent from Gansu — one of the most
underdeveloped provinces in China — to Shanghai in just 1.5 days. Shipping
would cost just $0.32.
In comparison, a box of 100
liquid serum specimens, which are seven times larger, takes 4–5 days, requires
cold-chain transportation, and costs $3.42.
Tests in
humans needed
Finding
a way to diagnose cancer that is cost-effective and accurate is exciting, but
there is much work to do before this technology enters the clinic.
In this study, they only
tested their AI model on a few hundred samples from people known to have
cancer.
Before this technology
moves into the mainstream, scientists need to test it on thousands of
real-world people. Bilchik, however, remains upbeat about the prospect:
“The results need to be
validated and prospectively studied because this could be practice-changing and
have a major impact on the diagnosis of missed cancers.”
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