A smart tele-cytology point-of-care platform for oral cancer screening.

• Main Authors: Sumsum Sunny, Arun Baby, Bonney Lee James, Dev Balaji, Aparna N V, Maitreya H Rana, Praveen Gurpur, Arunan Skandarajah, Michael D'Ambrosio, Ravindra Doddathimmasandra Ramanjinappa, Sunil Paramel Mohan, Nisheena Raghavan, Uma Kandasarma, Sangeetha N, Subhasini Raghavan, Naveen Hedne, Felix Koch, Daniel A Fletcher, Sumithra Selvam, Manohar Kollegal, Praveen Birur N, Lance Ladic, Amritha Suresh, Hardik J Pandya, Moni Abraham Kuriakose
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2019-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0224885

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.