Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions

BACKGROUND: Effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early stage cancer and improving outcomes. In this study, the authors describe cytopathology tools including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS: Data were acquired from a multi-site clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of four cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS: Cell phenotypes were accurately determined through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in three phenotypes (Type 1 ‘mature squamous’, Type 2 ‘small round’, and Type 3 ‘leukocytes’). The clinical algorithms resulted in acceptable performance characteristics (AUC = 0.81 for benign vs. mild dysplasia and 0.95 for benign vs. malignancy). Conclusion: These new cytopathology tools represent a practical solution for rapid PMOL assessment with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.