Literature DB >> 15019013

A randomized crossover trial of PAPNET for primary cervical screening.

Les Irwig1, Petra Macaskill, Annabelle Farnsworth, R Gordon Wright, Jan McCool, Alexandra Barratt, Judy M Simpson.   

Abstract

OBJECTIVE: To develop and demonstrate efficient methods to estimate the relative true positive and false positive rates of two cervical screening tests (conventional cytology and PAPNET).
METHODS: We designed the study to meet stringent methodologic criteria for comparison of two tests while simultaneously minimizing the numbers requiring reference standard verification. We used a cytology reference standard and also assessed histology when available. For the primary analysis, slides with discordant results around the test threshold (CIN 1) were reviewed by a panel of two cytopathologists, blind to previous results, to establish the reference standard result (reference standard threshold for abnormality CIN2). Where histology was available, a secondary analysis was conducted with the reference standard based on the highest grade lesion (either cytology or histology).
RESULTS: Among 21,747 Pap smears, 372 were discordant around the test threshold, requiring verification. In the primary analysis PAPNET detected four more true positives than conventional reading; difference in sensitivity 1.29% (95%CI -5.79 to 8.36%, P=.40). There were two extra false positives using PAPNET; difference in the false positive rate 0.0097% (95%CI -0.122 to 0.142%, P=.47). The results of the combined cytology and histology analysis were similar; difference in true positive rate 0.29% (95%CI -6.76 to 7.34%, P=.50) and difference in false positive rate 0.024% (95%CI -0.098 to 0.15%, P=.39).
CONCLUSION: This is an efficient and valid study design where the objective is to examine the comparative accuracy of two tests. The design provides an efficient means of estimating the difference between true positive and false positive detection by the two tests, which often is sufficient information for policy decisions.

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Mesh:

Year:  2004        PMID: 15019013     DOI: 10.1016/S0895-4356(03)00259-2

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  5 in total

Review 1.  Squamous cell carcinoma and precursor lesions: diagnosis and screening in a technical era.

Authors:  Catherine F Poh; Calum E MacAulay; Denise M Laronde; P Michele Williams; Lewei Zhang; Miriam P Rosin
Journal:  Periodontol 2000       Date:  2011-10       Impact factor: 7.589

2.  Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network.

Authors:  Eric S Wise; Kyle M Hocking; Colleen M Brophy
Journal:  J Vasc Surg       Date:  2015-05-05       Impact factor: 4.268

3.  Accuracy of reading liquid based cytology slides using the ThinPrep Imager compared with conventional cytology: prospective study.

Authors:  Elizabeth Davey; Jefferson d'Assuncao; Les Irwig; Petra Macaskill; Siew F Chan; Adele Richards; Annabelle Farnsworth
Journal:  BMJ       Date:  2007-06-29

Review 4.  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.  Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes.

Authors:  E Kontsek; A Pesti; J Slezsák; P Gordon; T Tornóczki; G Smuk; S Gergely; A Kiss
Journal:  Pathol Oncol Res       Date:  2022-08-17       Impact factor: 2.874

  5 in total

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