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Doctors,
scientists and researchers have built an artificial intelligence model that can
accurately identify cancer in a development they say could speed up diagnosis
of the disease and fast-track patients to treatment.
Cancer is a leading cause of death worldwide. It results in
about 10 million deaths annually, or nearly one in six deaths, according to the
World Health Organization.
In many cases, however, the disease can be cured if detected early and treated
swiftly.
The
algorithm performs more efficiently and effectively than current methods,
according to a study. The findings have been published in the Lancet’s
eBioMedicine journal.
“In the
future, we hope it will improve early detection and potentially make cancer
treatment more successful by highlighting high-risk patients and fast-tracking
them to earlier intervention,” said Dr Benjamin Hunter, a clinical oncology
registrar at the Royal Marsden and a clinical research fellow at Imperial.
The
team used CT scans of about 500 patients with large lung nodules to develop an
AI algorithm using radiomics. The technique can extract vital information from
medical images not easily spotted by the human eye.
The
AI model was then tested to determine if it could accurately identify cancerous
nodules.
The
study used a measure called area under the curve (AUC) to see how effective the
model was at predicting cancer. An AUC of 1 indicates a perfect model, while
0.5 would be expected if the model was randomly guessing.
The
results showed the AI model could identify each nodule’s risk of cancer with an
AUC of 0.87. The performance improved on the Brock score, a test currently used
in clinic, which scored 0.67. The model also performed comparably with the
Herder score – another test – which had an AUC of 0.83.
“According
to these initial results, our model appears to identify cancerous large lung
nodules accurately,” Hunter said. “Next, we plan to test the technology on
patients with large lung nodules in clinic to see if it can accurately predict
their risk of lung cancer.”
The
AI model may also help doctors make quicker decisions about patients with
abnormal growths that are currently deemed medium-risk.
When
combined with Herder, the AI model was able to identify high-risk patients in
this group. It would have suggested early intervention for 18 out of 22 (82%)
of the nodules that went on to be confirmed as cancerous,
The team
stressed that the Libra study – backed by the Royal Marsden Cancer Charity, the
National Institute for Health and Care Research, RM Partners and Cancer
Research UK – was still at an early stage. More testing will be required before
the model can be introduced in healthcare systems.
But its
potential benefits were clear, they said. Researchers hope the AI tool will
eventually be able to speed up the detection of cancer by helping to fast-track
patients to treatment, and by streamlining the analysis of CT scans.
“Through
this work, we hope to push boundaries to speed up the detection of the disease
using innovative technologies such as AI,” said the Libra study’s chief
investigator, Dr Richard Lee.
The
consultant physician in respiratory medicine at the Royal Marsden and team
leader at the Institute of Cancer Research said lung cancer was a good example
of why new initiatives to speed up detection were urgently needed.
Lung
cancer is the biggest worldwide cause of cancer mortality, and accounts for a
fifth (21%) of cancer deaths in the UK. Those diagnosed early can be treated much more effectively,
but recent data shows more than 60% of lung cancers in England are diagnosed at
either stage three or four.
“People
diagnosed with lung cancer at the earliest stage are much more likely to
survive for five years, when compared with those whose cancer is caught late,”
said Lee.
“This
means it is a priority we find ways to speed up the detection of the disease,
and this study – which is the first to develop a radiomics model specifically
focused on large lung nodules – could one day support clinicians in identifying
high-risk patients.”
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