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:
https://doi.org/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
Zdroje
1. Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral oncology. 2009;45(4–5):309–16. doi: 10.1016/j.oraloncology.2008.06.002 18804401
2. WHO. Cancer Fact sheet 2018. Available from: http://www.who.int/en/news-room/fact-sheets/detail/cancer.
3. Mignogna M, Fedele S, Russo LL. The World Cancer Report and the burden of oral cancer. European journal of cancer prevention. 2004;13(2):139–42. doi: 10.1097/00008469-200404000-00008 15100581
4. Babshet M, Nandimath K, Pervatikar S, Naikmasur V. Efficacy of oral brush cytology in the evaluation of the oral premalignant and malignant lesions. Journal of Cytology/Indian Academy of Cytologists. 2011;28(4):165.
5. Afrogheh A, Wright CA, Sellars SL, Wetter J, Pelser A, Schubert PT, et al. An evaluation of the Shandon Papspin liquid-based oral test using a novel cytologic scoring system. Oral surgery, oral medicine, oral pathology and oral radiology. 2012;113(6):799–807. doi: 10.1016/j.oooo.2012.01.027 22668708
6. Lee ES, Kim IS, Choi JS, Yeom BW, Kim HK, Han JH, et al. Accuracy and reproducibility of telecytology diagnosis of cervical smears: a tool for quality assurance programs. American journal of clinical pathology. 2003;119(3):356–60. doi: 10.1309/7ytvag4xnr48t75h 12645336
7. Heimann A, Maini G, Hwang S, Shroyer KR, Singh M. Use of telecytology for the immediate assessment of CT guided and endoscopic FNA cytology: Diagnostic accuracy, advantages, and pitfalls. Diagnostic cytopathology. 2012;40(7):575–81. doi: 10.1002/dc.21582 22707323
8. Bott MJ, James B, Collins BT, Murray BA, Puri V, Kreisel D, et al. A Prospective Clinical Trial of Telecytopathology for Rapid Interpretation of Specimens Obtained During Endobronchial Ultrasound–Fine Needle Aspiration. The Annals of thoracic surgery. 2015;100(1):201–6. doi: 10.1016/j.athoracsur.2015.02.090 26002445
9. Briscoe D, Adair CF, Thompson LD, Tellado MV, Buckner B, Rosenthal DL, et al. Telecytologic diagnosis of breast fine needle aspiration biopsies. Acta cytologica. 2000;44(2):175–80. doi: 10.1159/000326357 10740603
10. Galvez J, Howell L, Costa MJ, Davis R. Diagnostic concordance of telecytology and conventional cytology for evaluating breast aspirates. Acta cytologica. 1998;42(3):663–7. doi: 10.1159/000331823 9622684
11. Archondakis S, Georgoulakis J, Stamataki M, Anninos D, Skagias L, Panayiotides I, et al. Telecytology: a tool for quality assessment and improvement in the evaluation of thyroid fine-needle aspiration specimens. TELEMEDICINE and e-HEALTH. 2009;15(7):713–7. doi: 10.1089/tmj.2009.0037 19694595
12. Georgoulakis J, Archondakis S, Panayiotides I, Anninos D, Skagias L, Stamataki M, et al. Study on the reproducibility of thyroid lesions telecytology diagnoses based upon digitized images. Diagnostic cytopathology. 2011;39(7):495–9. doi: 10.1002/dc.21419 20730904
13. Yauney G, Angelino K, Edlund D, Shah P, editors. Convolutional Neural Network for Combined Classification of Fluorescent Biomarkers and Expert Annotations using White Light Images. Bioinformatics and Bioengineering (BIBE), 2017 IEEE 17th International Conference on; 2017: IEEE.
14. Pouliakis A, Karakitsou E, Margari N, Bountris P, Haritou M, Panayiotides J, et al. Artificial neural networks as decision support tools in cytopathology: past, present, and future. Biomedical engineering and computational biology. 2016;7:BECB. S31601.
15. Singh S, Tejaswini V, Murthy RP, Mutgi A. Neural network based automated system for diagnosis of cervical cancer. International Journal of Biomedical and Clinical Engineering (IJBCE). 2015;4(2):26–39.
16. Athinarayanan S, Srinath M. Classification of cervical cancer cells in PAP smear screening test. ICTACT Journal on Image and Video Processing. 2016;6(4):1234–8.
17. Żejmo M, Kowal M, Korbicz J, Monczak R, editors. Classification of breast cancer cytological specimen using convolutional neural network. Journal of Physics: Conference Series; 2017: IOP Publishing.
18. Subbaiah R, Dey P, Nijhawan R. Artificial neural network in breast lesions from fine‐needle aspiration cytology smear. Diagnostic cytopathology. 2014;42(3):218–24. doi: 10.1002/dc.23026 23908018
19. Im H, Pathania D, McFarland PJ, Sohani AR, Degani I, Allen M, et al. Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nature Biomedical Engineering. 2018:1. doi: 10.1038/s41551-018-0189-y
20. Skandarajah A, Sunny SP, Gurpur P, Reber CD, D'Ambrosio MV, Raghavan N, et al. Mobile microscopy as a screening tool for oral cancer in India: A pilot study. PloS one. 2017;12(11):e0188440. doi: 10.1371/journal.pone.0188440 29176904; PubMed Central PMCID: PMC5703562.
21. LeCun Y, Kavukcuoglu K, Farabet C, editors. Convolutional networks and applications in vision. 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010; 2010.
22. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z, editors. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016.
23. Afrogheh A, Wright CA, Sellars SL, Wetter J, Pelser A, Schubert PT, et al. An evaluation of the Shandon Papspin liquid-based oral test using a novel cytologic scoring system. Oral surgery, oral medicine, oral pathology and oral radiology. 2012;113(6):799–807. doi: 10.1016/j.oooo.2012.01.027 22668708.
24. Shirani S, Kargahi N, Razavi SM, Homayoni S. Epithelial dysplasia in oral cavity. Iranian journal of medical sciences. 2014;39(5):406–17. 25242838; PubMed Central PMCID: PMC4164887.
25. Speight PM. Update on oral epithelial dysplasia and progression to cancer. Head and neck pathology. 2007;1(1):61–6. doi: 10.1007/s12105-007-0014-5 20614284; PubMed Central PMCID: PMC2807503.
26. Kujan O, Oliver RJ, Khattab A, Roberts SA, Thakker N, Sloan P. Evaluation of a new binary system of grading oral epithelial dysplasia for prediction of malignant transformation. Oral oncology. 2006;42(10):987–93. doi: 10.1016/j.oraloncology.2005.12.014 16731030.
27. Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. Journal of clinical and diagnostic research: JCDR. 2016;10(10):YE01–YE6. doi: 10.7860/JCDR/2016/18129.8744 27891446; PubMed Central PMCID: PMC5121784.
28. Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. Journal of biomedical informatics. 2014;48:193–204. doi: 10.1016/j.jbi.2014.02.013 24582925.
29. Ana Justel DP, Ruben Zamar. A multivariate Kolmogorov-Smirnov test of goodness of fit. Statistics & Probability Letters. 1997;35(3):251–9. https://doi.org/10.1016/S0167-7152(97)00020-5.
30. Alli PM, Ollayos CW, Thompson LD, Kapadia I, Butler DR, Williams BH, et al. Telecytology: intraobserver and interobserver reproducibility in the diagnosis of cervical-vaginal smears. Human pathology. 2001;32(12):1318–22. doi: 10.1053/hupa.2001.29651 11774163.
31. Mun SK, Elsayed AM, Tohme WG, Wu YC. Teleradiology/telepathology requirements and implementation. Journal of medical systems. 1995;19(2):153–64. doi: 10.1007/bf02257066 7602247
32. Soille P, Vincent LM, editors. Determining watersheds in digital pictures via flooding simulations. Visual Communications and Image Processing '90; 1990: SPIE.
33. Rasband W. ImageJ offcial Github repository. Available from: https://imagej.net/Development.
34. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics: a review publication of the Radiological Society of North America, Inc. 2017;37(2):505–15. doi: 10.1148/rg.2017160130 28212054; PubMed Central PMCID: PMC5375621.
35. Joshi P, Nair S, Chaturvedi P, Nair D, Agarwal J, D'Cruz A. Delay in seeking specialized care for oral cancers: Experience from a tertiary cancer center. Indian journal of cancer. 2014;51(2):95. doi: 10.4103/0019-509X.137934 25104185
36. Bal MS, Goyal R, Suri AK, Mohi MK. Detection of abnormal cervical cytology in Papanicolaou smears. Journal of cytology. 2012;29(1):45–7. doi: 10.4103/0970-9371.93222 22438616; PubMed Central PMCID: PMC3307451.
37. Ogden G. The future role for oral exfoliative cytology—bleak or bright? Oral oncology. 1997;33(1):2–4. doi: 10.1016/s0964-1955(96)00047-4 9192544
38. Tsilalis T, Archondakis S, Meristoudis C, Margari N, Pouliakis A, Skagias L, et al. Assessment of static telecytological diagnoses' reproducibility in cervical smears prepared by means of liquid-based cytology. TELEMEDICINE and e-HEALTH. 2012;18(7):516–20. doi: 10.1089/tmj.2011.0167 22856666
39. Kldiashvili E, Schrader T. Reproducibility of telecytology diagnosis of cervical smears in a quality assurance program: the Georgian experience. Telemedicine and e-Health. 2011;17(7):565–8. doi: 10.1089/tmj.2011.0016 21851161
40. Sekine J, Nakatani E, Hideshima K, Iwahashi T, Sasaki H. Diagnostic accuracy of oral cancer cytology in a pilot study. Diagnostic pathology. 2017;12(1):27. doi: 10.1186/s13000-017-0618-3 28298213
41. Solomon D, Davey D, Kurman R, Moriarty A, O'connor D, Prey M, et al. The 2001 Bethesda System: terminology for reporting results of cervical cytology. Jama. 2002;287(16):2114–9. doi: 10.1001/jama.287.16.2114 11966386
42. Weigum SE, Floriano PN, Redding SW, Yeh C-K, Westbrook SD, McGuff HS, et al. Nano-bio-chip sensor platform for examination of oral exfoliative cytology. Cancer Prevention Research. 2010:1940–6207. CAPR-09-0139.
43. Lu Z, Carneiro G, Bradley AP, Ushizima D, Nosrati MS, Bianchi AG, et al. Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE journal of biomedical and health informatics. 2017;21(2):441–50. doi: 10.1109/JBHI.2016.2519686 26800556
44. Ushizima DM, Bianchi AG, Carneiro CM. Segmentation of subcellular compartments combining superpixel representation with voronoi diagrams. Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2015.
45. Nosrati M, Hamarneh G. A variational approach for overlapping cell segmentation. ISBI Overlapping Cervical Cytology Image Segmentation Challenge. 2014:1–2.
46. Hoo-Chang S, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging. 2016;35(5):1285. doi: 10.1109/TMI.2016.2528162 26886976
47. Kudva V, Prasad K. Pattern Classification of Images from Acetic Acid–Based Cervical Cancer Screening: A Review. Critical Reviews™ in Biomedical Engineering. 2018;46(2).
48. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115. doi: 10.1038/nature21056 28117445
49. Chougrad H, Zouaki H, Alheyane O. Deep convolutional neural networks for breast cancer screening. Computer methods and programs in biomedicine. 2018;157:19–30. doi: 10.1016/j.cmpb.2018.01.011 29477427
50. Weng S, Xu X, Li J, Wong ST. Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer. Journal of biomedical optics. 2017;22(10):106017.
51. Liu Y, Li J, Liu X, Liu X, Khawar W, Zhang X, et al. Quantitative risk stratification of oral leukoplakia with exfoliative cytology. PloS one. 2015;10(5):e0126760. doi: 10.1371/journal.pone.0126760 25978541
Článek vyšel v časopise
PLOS One
2019 Číslo 11
- Jak a kdy u celiakie začíná reakce na lepek? Možnou odpověď poodkryla čerstvá kanadská studie
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- Spermie, vajíčka a mozky – „jednohubky“ z výzkumu 2024/38
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Infekce se v Americe po příjezdu Kolumba šířily nesrovnatelně déle, než se traduje
Nejčtenější v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy