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Cellular Oncology
Volume 32, Issue 1-2, Pages 109-119
http://dx.doi.org/10.3233/CLO-2009-0508

A Virtual Microscopy System to Scan, Evaluate and Archive Biomarker Enhanced Cervical Cytology Slides

Niels Grabe,1,2 Bernd Lahrmann,1,2 Thora Pommerencke,1,2 Magnus von Knebel Doeberitz,3 Miriam Reuschenbach,3 and Nicolas Wentzensen3,4

1Institute of Medical Biometry and Informatics, Section Medical Informatics, University Hospital Heidelberg, Heidelberg, Germany
2Hamamatsu Tissue Imaging and Analysis Center, Heidelberg, Germany
3Institute of Pathology, Department of Applied Tumor Biology, University Hospital Heidelberg, Heidelberg, Germany
4Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Copyright © 2010 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

Conclusions: 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.