Qualitopix – Choice of Reference for Automatic Quality Assessment of the Estrogen Receptor
Astrid Ottosen1, Stine Harder1, Andreas Schønau1, Mogens Vyberg2, Rasmus Røge2, Søren Nielsen2
1 Visiopharm, Hørsholm, Denmark
2 NordiQC, Aalborg, Denmark
Background: Immunohistochemistry is an important tool in patient diagnostics, and external quality assessment (EQA) of immunoassays is essential to obtain optimal and comparable results. EQA is typically performed manually, which is partly subjective causing inconsistent scoring. Digital image analysis (DIA) may support EQA by extracting useful information to provide objective and standardized results. However, there may be limitations in using DIA due to the need for specialists’ interpretation of staining patterns.
Methods: Serial sections of tissue microarrays from three tissue blocks immunostained by 258 laboratories for Estrogen receptors (ER) were analyzed by DIA. Information about the intensity and quantification of stained tumor nuclei were assessed by calculating the H-score . Scores were evaluated by measuring the distance from the individual lab scores to an established reference, where a large distance indicates insufficiency. We investigated two different ways of defining the reference: A reference for each tissue block and a joined reference across all tissue blocks.
Results: The data were assessed by expert pathologists at NordiQC. The correlation was investigated by calculating a receiver operating characteristic curve. The method using the block specific reference revealed an area under the curve (AUC) of 0.89 whereas the joined reference yielded an AUC of 0.86.
Conclusion: Our image analysis method for quantifying staining sufficiency shows promising results for ER. A block specific reference yields the largest AUC.
Citations and references:
| N. Lykkegaard Andersen, A. Brügmann, G. Lelkaitis, S. Nielsen, M. Friis Lippert and M. Vyberg, “A Digital Approach to Immunohistochemical Quantification of Estrogen Receptor Protein in Breast Carcinoma Specimens,” Appl Immunohistochem Mol Morphol, 2017.|