An appraisal of pivotal evaluation designs in validating noninvasive biomarkers for head and neck cancer detection

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Efficacy of hypermethylated DNA biomarkers in saliva and oral swabs for oral cancer diagnosis: Systematic review and meta-analysis

OBJECTIVES

This study aims to determine the diagnostic test accuracy (DTA) of hypermethylated DNA biomarkers in saliva and oral swabs for oral squamous cell carcinoma (OSCC) detection from the prevalidation studies available.

MATERIALS AND METHODS

Electronic database searching of PubMed, EMBASE, Cochrane Library, Scopus, Web of Science, and LILACS was conducted to identify relevant articles that were published between January 1, 2000, and August 1, 2020.

RESULTS

Meta-analysis was conducted based on 11 of 20 studies selected for review. Included studies had high bias concerns on the QUADAS-2 study assessment tool. We found that salivary and oral swab hypermethylation markers had better specificity than sensitivity for oral cancer detection. Summary sensitivity and specificity (95% CI) of hypermethylation panels were 86.2% (60-96.2) and 90.6% (85.9-93.9) while for individual markers, summary sensitivity and specificity (95% CI) were 70% (56.9-80.5) and 91.9% (80.3-96.9), respectively. Respective positive and negative likelihood ratios for combined markers were 9.2 (5.89-14.36) and 0.15 (0.05-0.5), and 8.61 (3.39-21.87) and 0.33 (0.22-0.49) for single-application biomarkers.

CONCLUSION

DNA hypermethylation biomarkers especially in combination have acceptable DTA that warrants further optimization with rigorous biomarker evaluation methods for conclusive determination of their efficacy.