PLoS ONE (Jan 2019)

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

  • 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

DOI
https://doi.org/10.1371/journal.pone.0224885
Journal volume & issue
Vol. 14, no. 11
p. e0224885

Abstract

Read online

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.