New diagnosis method: a drop of blood detects early cancer

New diagnostic blood test paves the way for early lung cancer screening

According to a new study, a diagnostic blood test can provide early lung cancer detection in asymptomatic patients

Lung cancer is the main cause of cancer death, and it is usually diagnosed in the late stage when the survival rate is extremely low

The research, published in the Proceedings of The National Academy of Sciences, provides a proof of concept for a drop of blood showing the ability of asymptomatic patients to develop lung cancer

Leo Cheng, a pathologist and radiological biophysicist, said: "Our research demonstrates the potential of developing a sensitive screening tool for early detection of lung cancer

Cheng and their co-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

Cheng said: "This is very encouraging, because early disease screening should be able to detect changes in the blood metabolome profile between healthy and disease states

Then, the researchers tested their model with blood samples from 54 patients with non-small cell lung cancer, which were obtained before their cancer diagnosis, which confirmed that the model's predictions were accurate

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