A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions

Objectives A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation (MT). Distinguishing OLCs from other oral potentially malignant disorders (OPMD) can help prevent unnecessary concern or testing, but accurate identification by non-expert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a cytomics-on-a-chip tool and integrated predictive model for aiding the identification of OLCs. Study Design All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N=94) and non-lichenoid (OLC−) (N=237) histologic features in a population with OPMDs. Results The OLC Index discriminated OLC+ and OLC− subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (p=0.0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology. Conclusion The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis and risk stratification of patients presenting with OPMDs and OLCs.