A relatively simple blood test can help in screening for lung cancer, and the identification of individuals who need to undergo low-dose computed tomography.
With the blood test measured four levels of circulating proteins. Then, these measurements are used to calculate the risk of developing lung cancer.
New research has shown that this test more accurately detect and control future cases of lung cancer, compared with the traditional model of risk assessment based on the analysis of the history and current smoking in the US screening criteria.
"Profiling based biomarkers can improve patient selection for lung cancer screening" - the researchers reported.
There is an urgent need to improve the assessment of the risk of developing the disease, because the existing models are missing a significant proportion of cases. A new study shows that a group of biomarkers circulating proteins can be used to identify individuals who need to undergo low-dose computed tomography.
Risk assessment tool based on the biomarkers is based on four proteins: cancer antigen 125; carcinoembryonic antigen; fragment of cytokeratin-19; and surface protein precursor B. Scientists have developed it using preddiagnosticheskie blood samples obtained from patients with a high risk of developing lung cancer, among whom there were 108 smokers who have been diagnosed with cancer within a year of blood collection, and 216 smokers in the control group study CARET.
To test the effectiveness of a blood test, the researchers estimated the absolute risk of developing cancer in 63 patients who have ever smoked and who were diagnosed with cancer within a year after blood collection. These patients were compared with 90 patients in the control group study and NSHDS EPIC study. The average age of participants was 58 years, 69% were male.
The researchers then combined with biomarker indicators of smoking history data. Using this integrated model of risk prediction, they identified 40 cases of cancer out of 63 (63% of future cases of lung cancer) screening for low-dose computed tomography. This corresponded to a sensitivity of 0.63.
For comparison, only 26 out of 62 cases, or 42% of future cancer cases lekih were identified using standard estimation techniques. This corresponded to a sensitivity of 0.42.
The study results also show that the model of integrated cancer prediction can be used to reduce the number of false-positive screening, compared with the current model.