The same technique could also be used to diagnose other types of cancer, A blood test to detect early-stage lung cancer predicts disease that may be available within five years following a medical breakthrough to identify chemicals to use artificial intelligence.
According to researchers at Stanford University, this technique can also be used to detect other forms of disease such as liver, pancreatic and stomach cancer.
The standard method of screening for lung cancer in high-risk individuals is CT scanning.
But this technique is not widely used because it is expensive and has a high level of “false positives” – the fear that a person has cancer when they do not have it.
Liquid biopsies gaining popularity
As a result, researchers are looking for blood tests as an excellent alternative way to diagnose cancer.
However ‘liquid biopsy’ treatments in development typically only identify people with late stages of the disease when the level of DNA tumour in the blood is high, as cancer has advanced.
In contrast, Stanford researchers have developed a method of identifying tumour DNA much earlier.
They have done this using machine learning to predict the presence of DNA derived from lung cancer in future blood samples based on earlier changes in blood makeup.
“We think our research is fascinating because we know that the best way to treat lung cancer is to detect it early before it spreads outside the lungs,” said Max Diehn of Stanford University.
“Our method achieves a sensitivity of about 40 per cent for stage I, 55 per cent for stage II, and 65 per cent for stage III. Overall, this means that the test probably detects more than half of early-stage lung cancer. Can help in planting,” he said.
“If confirmed in future studies, it may help to detect the majority of lung cancer before they have spread,” added Dr. Diehn.
Get the best of The Thus delivered to your inbox – subscribe to The Thus Newsletters. For the latest News follow The Thus on Facebook, Twitter, Instagram, and Pinterest and stay in the know with what’s happening in the world around you – in real-time.