A New Method for the Early Detection of the Lung Cancer by the Saliva Tests Using Surface-Enhanced Raman Spectroscopy

Objective: To explore the new method for the early detection of lung cancer through the saliva test using Surface-Enhanced Raman Spectroscopy. Methods: Surface-enhanced Raman detection system developed by American OptoTrace Technologies, Inc. is using in this study. The saliva samples from 61 cases of lung cancer patients without treatment were collected by the Thoracic Surgery of Beijing Xuanwu Hospital and the saliva samples from 66 health people were collected as controls. The saliva was analyzed with nano-modified chip. Statistics was done with Support Vector Machine algorithm and leave-one-out test was used based on Support Vector Machine algorithm to analyze the difference of saliva between the lung cancer patients' and healthy human. Results: The sensitivity and specificity of the classification result using the Supports Vector Machine algorithm are both 100%, with the Raman spectroscopy data of lung cancer patients and normal saliva samples. Using leave-one-out test, the sensitivity is 95.08% and the specificity was 100%. Sensitivity of Random Forests method was 96.72% and specificity was 100%. Conclusion: This study demonstrated the feasibility and effectiveness of a new method on the early detection of the lung cancer. It is an important advantage that SERS detection technology can perform noninvasive and fast test on saliva samples.