Affiliations: Institute of Medical Biometry and Informatics, Section Medical Informatics, University Hospital Heidelberg, Heidelberg, Germany | Hamamatsu Tissue Imaging and Analysis Center, Heidelberg, Germany | Institute of Pathology, Department of Applied Tumor Biology, University Hospital Heidelberg, Heidelberg, Germany | Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
Note: [] Corresponding author: Dr. Niels Grabe, Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT BQ10, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. Tel.: +49 6221 54 51248; Fax: +49 6221 54 51482; E-mail: [email protected].
Abstract: 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.
