The Accuracy of Salivary Biomarkers for Detection of Oral Cancer: A Meta-Analysis
Keywords:
Salivary biomarker, Oral cancer, Meta-analysisAbstract
Background: There are various studies about salivary biomarkers in oral cancer detection. Breakthrough technologies have lead to an increased discovering salivary biomarkers, which change the conclusion of accuracy in oral cancer detection. Many studies support that saliva biomarkers have a potential of being an essential tools as diagnosis, prognosis and maintenance program for oral cancer. However, sufficient evidence is still lacking. Objective: This meta-analysis has an objective to gather the results about an accuracy of the use of saliva as a detection tool of oral cancer to determine the accuracy and determine which biomarker is the most likely to use in oral cancer detection. Method: The electronic databases were searched on January 2009 to 2019. We searched Pubmed, The Cochrane Database of Systematic Reviews, Google Scholar and manual search. Result: We included 14 articles which considered saliva as a diagnostic accuracy studies are included. Statistical analysis established sensitivity and specificity with 95% confident interval of the accuracy test of the use of saliva as an oral cancer detection tools were included in this meta-analysis. These 14 articles can be divided into 30 studies, which were meta analyzed by using Hierachical model. Meta analyzed data found that the use of saliva as an oral cancer detection tools has sensitivity of 0.72 (95% CI = 0.61, 0.81), specificity of 0.78 (95% CI = 0.61, 0.81), but data is lack of homogeneity and the confident interval is wide. Conclusion: There is no sufficient evidence to support the accuracy of salivary biomarker for the early diagnosis of oral cancer. Subgroup analysis should be tested to summarize the cause of the lack of homogeneity. Overall analysis showed that protein biomarkers have higher sensitivity and specificity compared to other saliva biomarkers group. However, further clinical study with higher amount sample size and lack of bias should be studied to find out the most accurate saliva biomarker group in terms of sensitivity and specificity which can benefit in development of oral cancer detection tool.
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