Skip to main content
SpringerLink
Log in

The Application of PAPNET to Diagnostic Cytology

  • Chapter
Artificial Neural Networks in Biomedicine

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

Diagnostic cytology is a branch of pathology that attempts to diagnose human diseases, mainly cancer or precancerous states of various organs, by microscopic examination of cell samples, rather than tissue biopsies [1],[2]. There are several methods of obtaining cell samples. The cells may be secured by scraping or brushing the surface of the target organs (such as the uterine cervix, the oesophagus or the bronchus, to name a few). Cells may also be obtained by means of a needle-syringe system that may be used for aspiration of fluids accumulated in a body cavity or deeply seated lesions. Cells of diagnostic value may also be contained in urinary sediment or sputum. Regardless of origin and type of procedure, the sample is usually examined in the form of smears or equivalent preparations that must be stained to enhance the diagnostic features of cells. This is not the place to describe in detail the microscopic features that are of diagnostic significance and the interested reader is referred to other sources [1],[2]. Suffice to say, that the differences between benign and malignant cells are reflected mainly in the configuration and staining qualities of the nucleus of the cells, a small structure measuring from 7 to 12 microns in diameter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Koss, L. G. Diagnostic Cytology and its Histopathologic Bases. Ed.4. Philadelphia, J.B. Lippincott, 1992.

    Google Scholar 

  2. Koss, L. G., Woyke, S., and Olszewski, W. Aspiration biopsy. Cytologic Interpretation and Histologic Bases. Ed. 2. New York and Tokyo, Igaku Shoin, 1992.

    Google Scholar 

  3. Koss, L. G. The Papanicolaou test for cervical cancer detection. A triumph and a tragedy. JAMA, 261:737–743, 1989.

    Article  Google Scholar 

  4. Fontana, R. S. Screening for lung cancer. Recent experience in the United States. In: Lung Cancer: Basic and Clinical Aspects, H.H. Hansen (ed.). Martinus Nijhoff Publishers, Boston, 1986, pp. 91–111.

    Chapter  Google Scholar 

  5. Koss, L. G., Melamed, M. R., Ricci, A., Melick, W. F. and Kelly, R. E. Carcinogenesis in the human urinary bladder. Observations after exposure to paraaminodiphenyl. N. Engl. J. Med., 272: 767–770, 1965.

    Article  Google Scholar 

  6. Shu, Y. J. Detection of Esophageal Carcinoma by the Balloon Technique in the People’s Republic of China. In: Koss, L.G. and Coleman, D.V. (eds). Advances in Clinical Cytology. Masson, New York, 1984, vol. 2, pp. 67–102

    Google Scholar 

  7. van der Graaf, Y., Vooijs, G. P., Gaillard, H. L. J. et al. Screening errors in cervical cytology smears. Acta Cytol., 31:434–438, 1987.

    Google Scholar 

  8. Jones, B. A. Rescreening in gynecologic cytology. Resceening of 3762 previous cases for current high-grade squamous intraepithelial lesions and carcinoma - A College of American Pathologists Q-Probes study of 312 institutions. Arch. Path. Lab. Med. 119:1097–1103, 1995.

    Google Scholar 

  9. Schwartz, P. E., Merino, M. J. and Mccrea Curren, M. G. Clinical management of patients with invasive cervix cancer following a negative Pap smear. Yale J. Biol. Med., 61:327–338, 1988.

    Google Scholar 

  10. Koss, L. G. Analytical and quantitative cytology: A historical perspective. Anal. Quant. Cytol. Histol., 4:251–256, 1982.

    Google Scholar 

  11. Koss, L. G. Automated cytology and histology: A historical perspective. Anal. Quant. Cytol. Histol., 9:369–374, 1987.

    Google Scholar 

  12. Husain, A. O. N. The history of automated cell scanners. In: Grohs, H.K. and Husain, O.A.N. (Eds). Automated Cervical Cancer Screening. New York, Tokyo, Igaku Shoin, 1994.

    Google Scholar 

  13. Wied, G. L., Bartels, P. H., Bahr, G. F., and Oldfied, D. G. Taxonomic intracellular analytic system (TICAS) for cell identification. Acta Cytol., 12:180–204, 1968.

    Google Scholar 

  14. Bartels, P. H., Koss, L. G. and Wied, G. L. Automated cell diagnosis in clinical cytology. In: Advances in Clinical Cytology, Koss, L.G. and Coleman, D.V. (Eds.): Butterworth, London, 1980, pp. 314–342.

    Google Scholar 

  15. Hecht-Nielsen, R. Neurocomputing: Picking the human brain. IEEE Spectrum, 19:26–32, 1988.

    Google Scholar 

  16. Hammerstrom, D. Neural networks at work. IEEE Spectrum, 30:2632, 1993.

    Google Scholar 

  17. Mango, L. J. Computer-assisted cervical cancer screening using neural network. Cancer Letters, 77:155–162, 1994.

    Article  Google Scholar 

  18. Koss, L. G. Application of neural net-based system of cell recognition (The PAPNET System) to non-gynecologic cytologic samples. Curr. Diagn. Pathol. 5:65–69, 1998.

    Article  Google Scholar 

  19. Koss, L. G., Lin, E., Schreiber, K., Elgert, P. and Mango, L. Evaluation of the PAPNET cytologic screening system for quality control of cervical smears. Am. J. Clin.Path, 101:220–229, 1994.

    Google Scholar 

  20. Bacus, J. W., Wiley, E. L., Galbright, W. et al. Malignant cell detection and cervical cancer screening. Anal. Quant. Cytol. 20:565–572, 1976.

    Google Scholar 

  21. Koss, L. G., Sherman, M. E., Cohen, M. B. et al. Significant reduction in the rate of false-negative cervical smears with neural network-based technology PAPNET testing system). Human Path., 28:1196–1203, 1997.

    Article  Google Scholar 

  22. Sherman, M. E., Mango, L. J., Kelly, D. et al. PAPNET analysis of reported negative smears preceding the diagnosis of high-grade squamous intraepithelial neoplasia or carcinoma. Mod. Path., 7:578–581, 1994.

    Google Scholar 

  23. Ryan, M. R., Stastny, J. F., Remmers, R. et al. PAPNET-directed rescreening of cervicovaginal smears. A study of 101 cases of atypical squamous cells of unknown significance. Am. J. Clin. Pathol., 105:711–718, 1996.

    Google Scholar 

  24. Boon, M. E. and Kok, P. G. Neural network processing can provide means to catch errors that slip through human screening of Pap smears. Diagn. Cytopathol., 9:411–416, 1993.

    Article  Google Scholar 

  25. Boon, M. E., Kok, L. P., Nygaard-Nielsen, M., Holm, K. and Holund, B. Neural network processing of cervical smears can lead to a decrease in diagnostic variability and an increase in screening efficiency: A study of 63 false-negative smears. Mod. Path., 7:957–961, 1994.

    Google Scholar 

  26. Kok, M. R. and Boon, M. E. Consequences of neural network technology for cervical screening. Cancer, 78:111–118, 1996.

    Article  Google Scholar 

  27. Kok, M. R., Boon, M. E., Schreiner-Kok, P. G., and Koss, L. G. Cytologic recognition of invasive squamous cancer of the uterine cervix: Comparison of results of manual screening and the neural net-based semi-automated screening system (PAPNET). Submitted to Human Pathology.

    Google Scholar 

  28. Ouwerkerk-Noordam, E., Boon, M. E. and Beck, S. Computer assisted primary screening of cervical smears using the PAPNET method: Comparison with conventional screening and evaluation of the role of the cytologist. Cytopathology, 211–218, 1994.

    Google Scholar 

  29. Bosanquet, N., Coleman, D. V., Doré, C. et al. The PRISMATIC Trial: Improving primary screening of cervical smears using PAPNET System. Submitted to Lancet.

    Google Scholar 

  30. Saccomanno G., Saunders, R. P., Archer, V. E., Auerbach, O., Kuschner, M. and Beckler, P. A. Cancer of the lung: The cytology of sputum prior to the development of carcinoma. Acta Cytol., 9:413–423, 1965.

    Google Scholar 

  31. Hoda, R. S., Saccomanno, G., Schreiber, K., Decker, D. and Koss, L. G. Automated sputum screening with PAPNET system. Hum. Pathol., 27:656–659, 1996.

    Article  Google Scholar 

  32. Koss, L. G. Diagnostic Cytology of the Urinary Tract with Histopathologic and Clinical Correlations. Philadelphia, Lippincott-Raven, 1996.

    Google Scholar 

  33. Hoda, R. S., Tahir-Kheli, N. and Koss, L. G. Urine screening on PAPNET: A study of 50 cases. Meeting Abstract. Acta Cytol., 39:201, 1995.

    Google Scholar 

  34. Bales, C. E. A semi-automated method for preparation of urine sediment for cytologic evaluation. Acta Cytol., 25:323–326, 1981.

    Google Scholar 

  35. Greenebaum, E., Schreiber, K., Shu, Y. J. and Koss, L. G. Use of the esophageal balloon in the diagnosis of carcinomas of the head, neck and upper gastrointestinal tract. Acta. Cytol., 28:9–15, 1984.

    Google Scholar 

  36. Koss, L. G., Morgenstern, N., Tahir-Kheli, N. et al. Evaluation of esophageal cytology using a neural-net based interactive scanning system (The PAPNET System); Its possible role in screening for esophageal and gastric cancer. Am. J.Clin. Path., 109:549–557, 1998.

    Google Scholar 

  37. Vyborny, C. J. and Giger, M. L. Computer vision and artificial intelligence in mammography. Am. J. Roentgeneol., 162:699–7081, 1994.

    Google Scholar 

  38. Baker, J. A., Kornguth, P. J., Lo, J. Y. et al. Breast cancer: Prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology, 196:817–822, 1995.

    Google Scholar 

  39. Chan, H. P., Lo, S. C., Sahiner, B., Lam, K. L. et al. Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network. Med. Phys, 22:1555–1567, 1995.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Pathology, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, 10467, USA

    Leopold G. Koss M.D. FRCPath (Hon)

Authors
  1. Leopold G. Koss M.D. FRCPath (Hon)
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computing and Mathematical Sciences, Liverpool John Moore’s University, Liverpool, L3 3AF, UK

    Paulo J. G. Lisboa BSc, PhD

  2. School of Electronic, Communication and Electrical Engineering, University of Plymouth, Plymouth, UK

    Emmanuel C. Ifeachor BSc, MSc, PhD

  3. Institute of Computer Science, Technical University of Lodz, Lodz, Poland

    Piotr S. Szczepaniak MSc, PhD, DSc

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag London

About this chapter

Cite this chapter

Koss, L.G. (2000). The Application of PAPNET to Diagnostic Cytology. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0487-2_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-005-7

  • Online ISBN: 978-1-4471-0487-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation