Cytological recognition of invasive squamous cancer of the uterine cervix: comparison of conventional light-microscopical screening and neural network-based screening.

Cytologic recognition of invasive or microinvasive cancer of the uterine cervix may present substantial difficulties. In this study, we compared conventional light-microscopical screening of 109,104 cervical smears and neural network-based screening (NNS) of 245,527 smears, all obtained by the spatula-Cytobrush method. Two populations of Dutch women were included in the study: those receiving smears within the framework of the Dutch population screening program ("routine smears") and those receiving smears for other reasons, discussed in the text ("interval smears"). There were 71 smears, from an equal number of biopsy-confirmed invasive squamous carcinomas, 28 of which were microinvasive. The "interval smears" yielded a statistical valid higher prevalence of invasive cancer than "routine smears." Except for 5 smears that contained no evidence of abnormality ("sampling errors"), no false-negative errors occurred in the 52 NNS cases, whereas 4 such errors occurred in the 19 conventionally screened cases. By measuring the amount of cancerous material present in each smear (mapping), it could be documented that NNS was effective even in smears with a small number of cancer cells, whereas the 4 conventional false-negative screening errors occurred in smears of this type. The study showed that cells derived from invasive cancer of the cervix may have large bland nuclei that do not fit the images commonly associated with squamous cancer cells. Neural network-based screening of cervical smears was more effective than conventional screening in the diagnosis of invasive squamous cancer of the uterine cervix.