Literature DB >> 10455204

Observer variation in cytologic grading for cervical dysplasia of Papanicolaou smears with the PAPNET testing system.

H Doornewaard1, Y T van der Schouw, Y van der Graaf, A B Bos, J G van den Tweel.   

Abstract

BACKGROUND: To assess the interobserver and intraobserver variation of Papanicolaou (Pap) smear screening with the computer-assisted (neural network based) PAPNET Testing System in diagnosing cervical smear abnormalities, results of agreement were compared with the interobserver and intraobserver variation of conventional smear analysis.
METHODS: Cervical smears obtained from women in 1996 were reevaluated both by conventional light microscopy and with use of the PAPNET Testing System by the same four investigators, and results were compared with the original screening diagnoses obtained by both methods.
RESULTS: The interobserver results for epithelial abnormalities (the degree of agreement between the cytologists), characterized by weighted kappa statistics, were 0.71 (95% CI: 0. 68-0.73) for PAPNET screening and 0.69 (95% CI: 0.66-0.72) for conventional screening. No significant differences were found among the individual results obtained by the four cytotechnologists (intraobserver variation) with conventional screening versus PAPNET reviewing.
CONCLUSIONS: Pap smear grading with the PAPNET Testing System has interobserver and intraobserver variation similar to that of conventional screening of Pap smears in routine use. Cancer (Cancer Cytopathol) Copyright 1999 American Cancer Society.

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Year:  1999        PMID: 10455204     DOI: 10.1002/(sici)1097-0142(19990825)87:4<178::aid-cncr3>3.0.co;2-1

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  5 in total

1.  Cell surface as a fractal: normal and cancerous cervical cells demonstrate different fractal behavior of surface adhesion maps at the nanoscale.

Authors:  M E Dokukin; N V Guz; R M Gaikwad; C D Woodworth; I Sokolov
Journal:  Phys Rev Lett       Date:  2011-07-08       Impact factor: 9.161

2.  Towards early detection of cervical cancer: Fractal dimension of AFM images of human cervical epithelial cells at different stages of progression to cancer.

Authors:  Nataliia V Guz; Maxim E Dokukin; Craig D Woodworth; Andrew Cardin; Igor Sokolov
Journal:  Nanomedicine       Date:  2015-05-08       Impact factor: 5.307

3.  Cervical cytology reproducibility and associated clinical and demographic factors.

Authors:  Hyunsoo Hwang; Michele Follen; Martial Guillaud; Michael Scheurer; Calum MacAulay; Calum MacAulay; Gregg A Staerkel; Dirk van Niekerk; Jose-Miguel Yamal
Journal:  Diagn Cytopathol       Date:  2019-10-22       Impact factor: 1.390

4.  Nominated texture based cervical cancer classification.

Authors:  Edwin Jayasingh Mariarputham; Allwin Stephen
Journal:  Comput Math Methods Med       Date:  2015-01-14       Impact factor: 2.238

Review 5.  Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

Authors:  Abraham Pouliakis; Efrossyni Karakitsou; Niki Margari; Panagiotis Bountris; Maria Haritou; John Panayiotides; Dimitrios Koutsouris; Petros Karakitsos
Journal:  Biomed Eng Comput Biol       Date:  2016-02-18
  5 in total

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