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Lung cancer is the leading cause of cancer deaths and is usually diagnosed at an advanced stage when the survival rate is extremely low
Cheng said: "Our research proves the potential of developing a sensitive screening tool for early detection of lung cancer
Cheng, Christiani and their collaborating researchers established a lung cancer prediction model based on blood metabolomics characteristics
Researchers screened tens of thousands of blood samples stored in the MGH biological sample bank and other sample banks, and found 25 non-small cell lung cancer (NSCLC) patients at the time of diagnosis and at least 6 months before the diagnosis
The researchers first trained their statistical model to identify lung cancer by measuring the metabolomics feature values in blood samples from patients at diagnosis and comparing them with blood samples from healthy controls
The value of the prediction model measured from the blood sample before diagnosis can also predict the patient's 5-year survival, which may help guide clinical strategies and treatment decisions
Next, the researchers will analyze the metabonomics characteristics of the clinical features of lung cancer to understand the entire metabolic spectrum of the disease, which may help select targeted treatments
