Literature DB >> 10198930

Automated cervical cytology: meta-analyses of the performance of the PAPNET system.

O Abulafia1, D M Sherer.   

Abstract

UNLABELLED: Our objective was to assess current knowledge regarding PAPNET (Neuromedical Systems, Inc.) automated cervical cytology screening methods and to assess the performance of this automated system in comparison with manual screening. To this goal, studies published in the English language regarding the PAPNET system, identified from a MEDLINE search through August 1998 were selected. Performance of the PAPNET system was assessed with various meta-analysis techniques, using the method of Mantel-Haenszel. In the primary screening modality, meta-analysis of the performance of the PAPNET system indicates that when compared with manual screening, the odds of obtaining a positive result were significantly greater. The Mantel-Haenszel odds ratio for combined studies was 1.19 (95 percent CI = 1.13 to 1.26, P < .001), corresponding to 20 percent greater odds of positive or suspicious slides with PAPNET system. The PAPNET system performs with almost two-fold less false-negative results. The Mantel-Haenszel odds ratio for combined studies was 0.41 (95 percent CI = 0.25 to 0.67, P < .005). Applied as a quality control modality rescreening all consecutive previously manually screened negative slides, depending on study design, the PAPNET system reclassified as abnormal between 0.1 and 5 percent. However, when the PAPNET system was used to rescreen known false-negative slides, PAPNET system rescreening can correctly identify between 20 and 90 percent of manually screened false-negative slides with an average reduction of 33 percent of the manually screened false-negative slides. We conclude that compared with manual screening, PAPNET identifies 20 percent more abnormal, has two-fold less false-negative, and reclassifies as abnormal one third of manually screened false-negative slides. TARGET AUDIENCE: Obstetricians &amp; Gynecologists, Family Physicians. LEARNING
OBJECTIVES: After completion of this article, the reader will be able to understand how the PAPNET system works and what is its approved use by the FDA, and to understand the associated benefits and shortcomings of the PAPNET system when compared with the traditional screening method.

Entities:  

Mesh:

Year:  1999        PMID: 10198930     DOI: 10.1097/00006254-199904000-00022

Source DB:  PubMed          Journal:  Obstet Gynecol Surv        ISSN: 0029-7828            Impact factor:   2.347


  5 in total

1.  An optimal transportation approach for nuclear structure-based pathology.

Authors:  Wei Wang; John A Ozolek; Dejan Slepčev; Ann B Lee; Cheng Chen; Gustavo K Rohde
Journal:  IEEE Trans Med Imaging       Date:  2010-10-25       Impact factor: 10.048

2.  Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images.

Authors:  Wei Wang; John A Ozolek; Gustavo K Rohde
Journal:  Cytometry A       Date:  2010-05       Impact factor: 4.355

Review 3.  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

Review 4.  Artificial neural network in diagnostic cytology.

Authors:  Pranab Dey
Journal:  Cytojournal       Date:  2022-04-02       Impact factor: 2.091

5.  Endocervicoscopy and Biopsy to Detect Cervical Intraepithelial Squamous Neoplasia in Nonvisible Squamocolumnar Junction With Unsatisfactory Colposcopy: A Pilot Study.

Authors:  Siavash Rahimi; Carla Marani; Francis Gardner; Chit Cheng Yeoh; Iolia Akaev; Sergio Votano
Journal:  Technol Cancer Res Treat       Date:  2018-01-01
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.