One of the deadliest diseases is lung cancer which has affected people from all around the world. The earlier it is detected, the better chance of survival is a patient has. Medical researchers have been looking into the possibility of developing blood test to detect lung cancer in its early stages. They are looking for machine learning studies to help in identifying early stage cancer. In their work, the researchers have developed a machine learning model on data associated with non-small cell lung cancer.
A group of scientists has developed a blood test or liquid biopsy to detect lung cancer at an early stage. They have developed an artificial intelligence program to screen people for lung cancer by analyzing their blood for DNA mutations that drive the disease.
The research was led by radiation oncologist, Maximilian Diehn, from the Stanford Cancer Institute in California and published in Nature. They utilize machine learning to drill down on tiny levels of DNA from tumors in bloodstream. The team has found the quantity of DNA revealed a host of facts about non-small cell lung cancer, in people who already had the disease, including cell type, how advanced it was and how aggressively it was likely to spread.
The team trained a machine learning model to rate the chance that DNA variants in blood came from lung cancer, a method called as “lung cancer likelihood in plasma” or “Lung-CLiP”. The Lung-CLiP blood test could refine that screening process. Those with a positive Lung-CLiP could be referred more confidently for LDCT, a hybrid approach that could raise the number of lives saved annually in US. According to the researchers, this could work for cancer beyond the lung as well.
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