Simulation of primary cervical cancer screening by the PAPNET system in an unscreened, high-risk community.
OBJECTIVE
To evaluate the performance of the PAPNET system as a primary cervical cancer screening modality in an unscreened population with a high prevalence of cervical cancer and its precursor lesions.


METHODS
Consecutive cervical smears from 3,106 women, screened and reported in the usual manner, were submitted for analysis by the PAPNET system. The original manual screening diagnoses were compared with those obtained by PAPNET analysis. By inclusion of normal and abnormal smears, this evaluation not only provided quality assurance for the laboratory but also simulated primary screening by automation.


RESULTS
Comparison of the two methods of screening showed statistically significant superiority of the PAPNET over conventional screening (89.6% vs. 63.8%, respectively) in low grade lesions, including atypical squamous and atypical glandular cells of uncertain significance (ASCUS and AGUS, respectively) and low grade squamous intraepithelial lesion. Conversely, there was no significant difference between PAPNET and manual detection (87.5% vs. 94.6%) for more significant abnormalities, including high grade squamous intraepithelial lesions and invasive carcinoma.


CONCLUSIONS
The PAPNET system, which would probably not be affordable as a quality assurance modality only in the public health sector of this country, was shown to be more than sufficiently effective as a primary screening method for the large numbers of women likely to undergo cervical cancer screening in anticipated mass population programs.
Michelow
P M
PM
Department of Anatomical Pathology, School of Pathology, South African Institute for Medical Research, Johannesburg, South Africa.
Hlongwane
N F
NF
Leiman
G
G
D001331:Automation; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008328:Man-Machine Systems; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011785:Quality Assurance, Health Care; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013019:South Africa / Q000453:epidemiology; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology; D014626:Vaginal Smears / Q000295:instrumentation*; D014741:Video Recording