Abstract
BACKGROUND: This study assessed the efficacy of using oral liquid-based brush cytology (OLBC) coupled with immunostained cytology-derived cell-blocks, quantified using machine-learning, in the diagnosis of oral lichen planus (OLP). METHODS: Eighty-two patients diagnosed clinically with either OLP or oral lichenoid lesion (OLL) were included. OLBC samples were obtained from all patients before undergoing surgical biopsy. Liquid-based cytology slides and cell-blocks were prepared and assessed by cytomorphology and immunocytochemistry for four antibodies (Ki-67, BAX, NF-κB-p65, and AMACR). For comparison purposes, a sub-group of 31 matched surgical biopsy samples were selected randomly and assessed by immunohistochemistry. Patients were categorized according to their definitive diagnoses into OLP, OLL, and clinically lichenoid, but histopathologically dysplastic lesions (OEDL). Machine-learning was utilized to provide automated quantification of positively stained protein expression. RESULTS: Cytomorphological assessment was associated with an accuracy of 77.27% in the distinction between OLP/OLL and OEDL. A strong concordance of 92.5% (κ = 0.84) of immunostaining patterns was evident between cell-blocks and tissue sections using machine-learning. A diagnostic index using a Ki-67-based model was 100% accurate in detecting lichenoid cases with epithelial dysplasia. A BAX-based model demonstrated an accuracy of 92.16%. The accuracy of cytomorphological assessment was greatly improved when it was combined with BAX immunoreactivity (95%). CONCLUSIONS: Cell-blocks prepared from OLBC are reliable and minimally-invasive alternatives to surgical biopsies to diagnose OLLs with epithelial dysplasia when combined with Ki-67 immunostaining. Machine-learning has a promising role in the automated quantification of immunostained protein expression.