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


Autoři: Sumsum Sunny aff001;  Arun Baby aff004;  Bonney Lee James aff002;  Dev Balaji aff004;  Aparna N. V. aff004;  Maitreya H. Rana aff004;  Praveen Gurpur aff005;  Arunan Skandarajah aff006;  Michael D’Ambrosio aff006;  Ravindra Doddathimmasandra Ramanjinappa aff002;  Sunil Paramel Mohan aff007;  Nisheena Raghavan aff008;  Uma Kandasarma aff009;  Sangeetha N. aff010;  Subhasini Raghavan aff010;  Naveen Hedne aff001;  Felix Koch aff011;  Daniel A. Fletcher aff006;  Sumithra Selvam aff012;  Manohar Kollegal aff005;  Praveen Birur N. aff001;  Lance Ladic aff013;  Amritha Suresh aff001;  Hardik J. Pandya aff004;  Moni Abraham Kuriakose aff001
Působiště autorů: Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India aff001;  Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India aff002;  Manipal Academy of Higher Education, Manipal, Karnataka, India aff003;  Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India aff004;  Siemens Healthcare Pvt Ltd, Bangalore, India aff005;  Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America aff006;  Department of Oral and Maxillofacial pathology, Sree Anjaneya Dental College, Kozhikode, Kerala, India aff007;  Department of Pathology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India aff008;  Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India aff009;  Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India aff010;  University of Mainz, 55099, Mainz, Germany aff011;  Division of Epidemiology and Biostatistics, St. John’s Research Institute, St. John’s National Academy of Health Sciences, Bangalore, India aff012;  Siemens Healthineers, Malvern, Pennsylvania, United States of America aff013
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224885

Souhrn

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.

Klíčová slova:

Artificial neural networks – Cancer detection and diagnosis – Cytology – Diagnostic medicine – Dysplasia – Histology – Lesions – Pathologists


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