Literature DB >> 9438458

The AutoPap 300 QC System multicenter clinical trials for use in quality control rescreening of cervical smears: I. A prospective intended use study.

S F Patten1, J S Lee, D C Wilbur, T A Bonfiglio, T J Colgan, R M Richart, H Cramer, S Moinuddin.   

Abstract

BACKGROUND: The AutoPap 300 QC System is an automated device for the analysis of conventionally prepared cervical cytology slides. The AutoPap System selects an enriched population of cases for human quality control (QC) review. The device assigns a score based on the likelihood that a slide is abnormal. Cases are selected for QC rescreening that have scores exceeding a preset threshold corresponding to approximately the top 10% (or greater) of scores.
METHODS: AutoPap false-negative detection, compared with a 10% random QC process, was tested in a 6-center clinical evaluation study. At each site, a block of up to 150 consecutive negative slides (including detected false-negative cases) were selected randomly daily. All slides were run on the AutoPap System and rescreened by cytotechnologists for truth determination. The false-negative cases included in the top 10% group selected by AutoPap System then were compared with false-negative detection by the random selection process.
RESULTS: Fourteen thousand nine hundred fourteen cases were analyzed. The AutoPap-enriched 10% quality control group contained false-negative cases at rates 3 to 5 times that of the random selection method (P < 0.01). The sensitivity for all false-negative cases was 35% and was 52% for false-negative cases at the level of low grade squamous intraepithelial lesion and higher.
CONCLUSIONS: The AutoPap 300 QC System provides the potential for a marked increase in the number of false-negative cervical cytology cases that can be detected on QC rescreening. A significant reduction in laboratory false-negative rates can be expected if this device is utilized in routine practice.

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Year:  1997        PMID: 9438458     DOI: 10.1002/(sici)1097-0142(19971225)81:6<337::aid-cncr7>3.0.co;2-i

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


  2 in total

1.  Proof-of-principle study of a novel cervical screening and triage strategy: Computer-analyzed cytology to decide which HPV-positive women are likely to have ≥CIN2.

Authors:  Mark Schiffman; Kai Yu; Rosemary Zuna; S Terence Dunn; Han Zhang; Joan Walker; Michael Gold; Noorie Hyun; Greg Rydzak; Hormuzd A Katki; Nicolas Wentzensen
Journal:  Int J Cancer       Date:  2016-10-17       Impact factor: 7.396

2.  A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images.

Authors:  Fahdi Kanavati; Naoki Hirose; Takahiro Ishii; Ayaka Fukuda; Shin Ichihara; Masayuki Tsuneki
Journal:  Cancers (Basel)       Date:  2022-02-24       Impact factor: 6.639

  2 in total

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