Researchers from Massachusetts General Hospital in Boston have shown that a predictive model can detect early lung cancer from a blood sample.
Lung cancer is usually diagnosed after it has progressed to a low-survivability state. Current methods for detecting early-stage lung cancer, which is mostly asymptomatic, are costly and pose a radiation hazard from repeated screenings. The MGH study shows a proof of concept for lung cancer detection in asymptomatic patients from a drop of blood, the hospital said.
Researchers trained the model using blood samples from lung cancer patients at the time of diagnosis. The model was then able to detect differences in blood samples from the lung cancer patients taken at least six months before their diagnoses and blood from healthy patients.
"Our study demonstrates the potential for developing a sensitive screening tool for the early detection of lung cancer," co-lead researcher Leo Cheng, PhD, said. "The predictive model we constructed can identify which people may be harboring 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 study was published in the December 13 issue of Proceedings of the National Academy of Sciences.
The researchers are planning to use the same technology to screen for Alzheimer's disease using blood samples and cerebrospinal fluid.