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

Abstract

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.

Fig 1. Study design.

Microscopic slides were prepared (a) using liquid based cytology and slides were reviewed by (b) conventional direct microscopy. Images were captured using CellScope (c) and sent to remote server (d). Tele-cytology diagnosis (f) were performed by pathologist. Image pre-processing algorithm were developed, and ANN based cytology diagnostic platform were developed (g) and validated. Conventional cytology diagnosis, tele-cytology diagnosis and ANN based diagnosis were compared with histopathology.

Fig 2. Workflow of image processing and ANN.

Complete workflow (a) diagram of the automated diagnosis system; The cells are extracted from the tele-cytology images and are fed into a neural network and the values from all the cells in a patient are aggregated and used for developing risk stratification model. Image pre-processing algorithm (b) consisting of Field of View (FOV) extraction from the tele-cytology images, detection of contrasting cellular mass from the background, detachment of connected Region of Interests (ROIs), removing the artefacts outside the ROIs, and extracting cell ROIs. The graph represents validation accuracy during training (epochs = 4,000) of the ANN (c).

Fig 3. A batch of trained and validated cell images.

Images of atypical cells(a) and normal cells (b) used for training the ANN. Cells classified by ANN: cells having atypical score less than 0.3 (c) from benign subjects, cells with atypical score between 0.3 to 0.5 from LGD patients(d), cells with atypical score between 0.5 to 0.7 (e) from HGD patients, cells with atypical score between 0.7 to 0.9 (f) from OSCC patients and cells with atypical score greater than 0.9 (g) from OSCC patients.

Fig 4. Distribution of manual and ANN cytology scores.

Box and whisker plot represent (a) cytology score of direct microscopy method (n = 82), OSCC (4.08±1.92) score shows significant difference from (*p<0.005) LGD (0.63±1.12) and HGD lesions (1±1.05). ANN Scoring (n = 60): The mean score of all cells (b) shows statistical significance between dysplasia (HGD, LGD) and OSCC (0.40±0.08, *p<0.005), but does not show significant difference between LGD (0.17±0.09) and HGD (0.21±0.08). The percentage of atypical cells (c) OSCC (0.38±0.11) shows significant difference from dysplasia (*p<0.005) but not show significant between HGD (0.17±0.09), and LGD (0.17±0.09). The mean atypical score of atypical cells (cells having score >0.05) (d) in each patient demonstrating statistical significance between dysplasia (HGD, LGD) and OSCC (0.71±0.02, *p<0.005) and also between LGD (0.78±0.03) and HGD (0.76±0.03) (**p<0.05). The mean and standard deviation values are provided in brackets.

Fig 5. Risk stratification model.

Scatter plot (a) representing percentage of atypical cell and mean score of all cells (n = 60) showing high correlation (r = 0.992, CI = 0.986–0.995) and these variables used for test 1, in risk stratification model (SVM). The cut-off value of ROC curve analysis (b) in delineating OSCC from LGD were used in risk stratification model as test 2 (c).