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BACKGROUND: Although cytological screening for cervical precancers has led to a reduction of cervical cancer incidence worldwide it is a subjective and variable method with low single-test sensitivity. New biomarkers like p16 that specifically highlight abnormal cervical cells can improve cytology performance. Virtual microscopy offers an ideal platform for assisted evaluation and archiving of biomarker-stained slides. METHODS: We first performed a quantitative analysis of p16-stained slides digitized with the Hamamatsu NDP slide scanner. From the results an automated algorithm was created to reliably detect cells, nuclei and p16-stained cells. The algorithm's performance was evaluated on two complete slides and tiles from 52 independent slides (11,628, 4094 and 25,619 cells/clusters, respectively). RESULTS: We achieved excellent performance to discriminate unstained cells from nuclei and biomarker-stained cells. The automated algorithm showed a high overall and positive agreement (99.0-99.7% and 70.9-83.4%, respectively) with the gold standard and had a very high sensitivity (89.1-100.0%) and specificity (98.9-100.0%) to detect biomarker-stained cells. CONCLUSION: We implemented a virtual microscopy system allowing highly efficient automated prescreening and archiving of biomarker-stained slides. Based on the initial results, we will evaluate the performance of our system in large epidemiologic studies against disease endpoints.
Grabe et al. (Fri,) studied this question.