Dr Chris discusses CT scans detecting lung cancer
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Scientists have long been plagued by the difficulty in detecting early-stage lung cancer. At this stage, the disease is mostly asymptomatic, and low-dose spiral CT imaging, which is the current method used to try and detect early lung cancer lesions, isn’t possible for widespread screening tests for the general population due to high cost and the radiation hazard of repeated screenings.
But a new study published in Proceedings of the National Academy of Sciences has come up with a ground-breaking method that could be used for detecting cancer at this early stage, giving doctors the ability to discover lung cancer in asymptomatic patients with just a single drop of blood.
The study was co-led by researchers at Massachusetts General Hospital (MGH).
Co-author Dr Leo Cheng, PhD, associate biophysicist, said: “Our study demonstrates the potential for developing a sensitive screening tool for the early detection of lung cancer.
“The predictive model we constructed can identify which people may be harbouring lung cancer. Individuals with suspicious findings would then be referred for further evaluation by imaging tests, such as low-dose CT, for a definitive diagnosis.”
The researcher team built a lung-cancer predictive model that was based on metabolomics profiles in blood.
Metabolomics analyses cellular metabolite flows to find healthy and pathological states by studying the metabolome – a feature found in all cells, fluids, and tissues of the body.
When lung cancer is present in an individual its altered physiology and pathology can cause changes in the blood metabolites that are produced or consumed by cancer cells in the lungs.
The researchers then measured metabolomics profiles in blood using high-resolution magnetic resonance spectroscopy.
This is a tool that can examine a large number of compounds inside living cells by measuring reactions of metabolites.
By screening tens of thousands of blood specimens stored in MGH’s biobank and others, the researchers found 25 patients with non-small cell lung cancer (NSCLC) with stored blood specimens that were taken at the time of their diagnosis and at least six months prior to their diagnosis.
They matched these patients with 25 healthy controls.
The research team trained their statistical model to identify lung cancer by measuring metabolomic profile values in blood samples taken from patients at the time of their diagnosis and comparing them to blood samples from the healthy controls.
They then used blood samples from the same patients that were taken before their lung cancer diagnosis.
Their predictive model showed values between the healthy controls and the patients at the time of their diagnosis.
Dr Cheng said: “This was very encouraging, because screening for early disease should detect changes in blood metabolomic profiles that are intermediate between healthy and disease states.”
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The next step for the researchers are planning is to analyse the metabolomic profiles of lung cancer’s clinical characteristics to understand the complete metabolic spectrum of the disease.
This could be essential in helping doctors choose targeted therapies for lung cancer patients.
The investigators also plan to use the same technology to screen for Alzheimer disease using blood samples and cerebrospinal fluid.
Around 47,000 people are diagnosed with lung cancer every year in the UK.
More than 4 out of 10 people diagnosed with lung cancer in the UK are aged 75 and older.
It’s rare in people younger than 40.
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