title: Switching from neural networks (PAPNET) to the Imager (Hologic) for computer-assisted screening.
abstract: OBJECTIVE
  The large set of ThinPrep slides prepared in the Leiden Cytology and Pathology Laboratory is exploited for calculating the impact of the transition from PAPNET neural network scanning to the Imager technology.
  
  
  METHODS
  All cervical samples were suspended and fixed in the coagulant fixative BoonFix. We compared 57,541 ThinPrep slides which were scanned by PAPNET and 64,273 ThinPrep slides processed with the Imager: 99,157 cases originated from the Dutch population screening program of asymptomatic women (screenees) and the remaining 22,657 samples were of symptomatic women. In the PAPNET series, 23% were diagnosed by additional light microscopy; in the Imager method, all slides were studied light microscopically. The cytoscores (positive cytology per 1,000 samples) were calculated for normal, atypical squamous cells of undetermined significance (ASC-US), cervical intraepithelial neoplasia (CIN) grades I-II, and for CIN III+. The odds ratios (ORs) for the positive cytoscores were assessed for both the screenees and the symptomatic women.
  
  
  RESULTS
  The cytoscores, per 1,000 cases, for ASC-US varied from 17.77 to 40.59, for CIN I-II from 7.17 to 33.35, and for CIN III+ from 2.81 to 8.8. These 6 cytoscores were higher for symptomatic women than for screenees. We observe significantly elevated ORs for the Imager for ASC-US (1.26 and 1.23), CIN I-II (1.45) and for CIN III+ (1.58 and 1.45). These 3 ORs are higher for screenees than for symptomatic women.
  
  
  CONCLUSIONS
  The Imager technology is more efficacious, particularly for handling screenee slides.
authors.affiliation: Leiden Cytology and Pathology Laboratory (LCPL), Leiden, The Netherlands. M.E.Boon@lcpl.nl
authors.forename: Mathilde E
authors.initials: ME
authors.lastname: Boon
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Ouwerkerk-Noordam
authors.forename: Elisabeth M
authors.initials: EM
authors.lastname: Meijer-Marres
authors.forename: T Romke
authors.initials: TR
authors.lastname: Bontekoe
mesh_terms: D002584:Cervix Uteri / Q000473:pathology; D016001:Confidence Intervals; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D016017:Odds Ratio; D014626:Vaginal Smears / Q000379:methods*