Literature DB >> 10574595

The diagnostic value of computer-assisted primary cervical smear screening: a longitudinal cohort study.

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

Abstract

OBJECTIVES: To assess computer-assisted (neural network based) cervical smear screening as a primary tool for the early detection of cervical dysplasia.
DESIGN: Longitudinal cohort study.
SETTING: Cytology laboratory reviewing cervical smears taken by general practitioners in a mass screening program in the Netherlands.
SUBJECTS: 846 women who developed (pre-)neoplasia of the cervix in the seven years after the baseline smear, and 5217 controls.
INTERVENTIONS: Cervical smears were evaluated both by conventional light microscopy and with use of the PAPNET Testing System by the same cytotechnologists. MAIN OUTCOME MEASURES: Seven year histological and cytological follow-up results were obtained for all women from a nation-wide pathology database.
RESULTS: Conventional screening diagnosed dysplasia or carcinoma in the baseline smears of 458 (54.1%) of the 846 women who were diagnosed with (pre-)neoplasia during follow-up, whereas computer-assisted PAPNET analysis detected such lesions in 462 (54.6%) of these women. In the control population of 5217 (86.0%) women, in whom follow-up revealed no cervical dysplasia, conventional screening gave false positive results in 210 (4.0%) and computer-assisted PAPNET analysis gave false positive results in 207 (4.0%) smears. The areas under the receiver operation curves (AUC) were 80% (95% confidence interval, 78 to 82%) and 79% (95% confidence interval, 77 to 81%) for conventional and PAPNET-assisted screening, respectively.
CONCLUSIONS: The PAPNET Testing System has similar diagnostic value as the conventional screening of Pap smears when used for primary screening.

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Mesh:

Year:  1999        PMID: 10574595

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  2 in total

Review 1.  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 2.  Artificial neural network in diagnostic cytology.

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

  2 in total

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