title: How to improve cytologic screening for endocervical adenocarcinoma?
abstract: OBJECTIVE
  A retrospective study was undertaken to investigate how to improve the diagnosis of endocervical adenocarcinoma in screening programs.
  
  
  METHODS
  The study group consisted of 29 slides of women diagnosed with cancer but who had negative smears. The slides were subdivided in 12 smears taken less than one year before diagnosis by histology and 17 smears taken between one and 10 years prior to diagnosis. A hundred smears of healthy women were used for comparison. All smears were studied macroscopically after which both groups of smears were scanned by the Neural Network Scanner (NNS). Differences between groups were studied for statistical significance using Pearson's Chi-squared test.
  
  
  RESULTS
  The macroscopic parameter of these smears found to be present most frequently was a heavy admixture of blood. The presence of blood (lysed or not) in the smears was equally consistently highlighted by the NNS. Statistical significance of the association of this parameter, with the presence of cancer, was demonstrated.
  
  
  CONCLUSIONS
  The awareness of blood as a background feature of adenocarcinoma of the cervix will help to select cases needing special attention. These difficult bloody smears, studied by light microscopy and by NNS images can also be selected for additional MiB-1 staining. With this approach, blood in smears, otherwise frequently leading to a compromise of classification, can become a blessing in disguise. The diagnosis of endocervical adenocarcinoma in screening smears will therefore be improved.
authors.affiliation: Leiden Cytology and Pathology Laboratory, Leiden, The Netherlands.
authors.forename: M E
authors.initials: ME
authors.lastname: Boon
authors.forename: E
authors.initials: E
authors.lastname: Ouwerkerk-Noordam
authors.forename: A W F M
authors.initials: AW
authors.lastname: van Leeuwen
authors.forename: L P
authors.initials: LP
authors.lastname: Kok
authors.forename: C
authors.initials: C
authors.lastname: van Haaften-Day
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis* / Q000473:pathology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D009426:Netherlands; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011785:Quality Assurance, Health Care; D012189:Retrospective Studies; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology; D014626:Vaginal Smears / Q000592:standards*