New paradigm for ASCUS diagnosis using neural networks.
It was tested whether it was possible to reduce the atypical squamous cells of undetermined significance (ASCUS) scores in a meaningful way by exploiting the cells selected by the neural networks of the PAPNET system. For this test, 2,000 routine smears were screened once by means of PAPNET and once conventionally in a laboratory in Amsterdam. From these 2,000 smears, 168 were diagnosed as ASCUS. In the second phase of the study, the diagnosis was based solely on the PAPNET images, and in addition, cases with immature cells (bare nuclei and cells with very little cytoplasm) in the PAPNET images were classified as ASCUS. Although, in this second phase, 75.6% of the cases were revised to negative, the cases with positive follow-up were all still classified as ASCUS. The negative predictive value remained at 100%, whereas the positive predictive value increased from 14.3 to 30%. By using the new paradigm (focusing on immature cells selected by the neural networks) for routine primary PAPNET screening in a laboratory in Leiden, the ASCUS scores were reduced from 10% (June of 1996) to 1.0% (early 1998), with promising follow-up results for the first half of 1997.
Kok
M R
MR
Leiden Cytology and Pathology Laboratory, The Netherlands.
Habers
M A
MA
Schreiner-Kok
P G
PG
Boon
M E
ME
D002584:Cervix Uteri / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004847:Epithelial Cells / Q000473:pathology*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D009426:Netherlands; D016571:Neural Networks, Computer; D011230:Precancerous Conditions / Q000175:diagnosis / Q000473:pathology; D011237:Predictive Value of Tests; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis* / Q000473:pathology; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology; D014626:Vaginal Smears / Q000379:methods*