Cytopathological quantification of NORs using artificial intelligence to oral cancer screening

Abstract

Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. This study included four experimental groups: control, exposure to carcinogens (alcohol and tobacco), oral potentially malignant disorders, and OSCC. In the first phase, 50 cells were used for AgNOR quantification. In the second phase, AgNOR quantification was established in an automated manner using an AgNOR System – Slide Examiner (captured – bounding-boxed – CNN analysis). In phase 1, the cut-off point for considering a smear as suspicious was established at 3.69 AgNORs/nucleus with sensitivity of 86%, specificity of 93%, and accuracy of 90%. In phase 2, the analysis of the intraclass correlation coefficient of AgNORs attributed to the system and human was 0.896 (95% confidence interval = 0.875–0.915; p < 0.0001), and this quantification with the CNN was 20 min compared to 67 h, considering human analysis. The AgNOR Slide-Image Examiner successfully differentiated the nuclei and accurately quantified the number of NORs in oral cytological smears. The cut-off risk point of 3.69 AgNOR/nucleus indicates a suspicious sample may contribute to improvements in oral cancer screening.