Optical density-based image analysis method for the evaluation of hematoxylin and eosin staining precision.

Staining quality and reproducibility are essential factors to monitor laboratory quality assurance. In the last decade, there has been an increase in the use of digital pathology and image analysis. While the adoption of these tools provides a potential means to track staining precision by optical density (OD), it also presents challenges. Results from image analysis are more sensitive to variations in staining than microscopic evaluation by a pathologist. There are two goals with this study. The first was to track the precision of hematoxylin and eosin (H&E) staining, in both nuclear and cytoplasmic components by OD. The second was to determine the impact of different pre-analytical and analytical variables on the OD results. Specifically, the endpoints investigated were quality parameters including impacts of section thickness, protocol manipulation, expired hematoxylin on staining precision and reproducibility of staining over time. Our results show that image analysis of H&E-stained tissue sections is a viable tool for assessing and verifying staining quality. We also show that OD analysis results for H&E-stained sections are affected by changing pre-analytical and/or reagent variables. These authors chose a graphical rather than fully statistical analysis of the results to highlight the utility of visual aids in demonstrating H&E staining reproducibility.

An Image Analysis Solution For Quantification and Determination of Immunohistochemistry Staining Reproducibility.

With immunohistochemical (IHC) staining increasingly being used to guide clinical decisions, variability in staining quality and reproducibility are becoming essential factors in generating diagnoses using IHC tissue preparations. The current study tested a method to track and quantify the interrun, intrarun, and intersite variability of IHC staining intensity. Our hypothesis was that staining precision between laboratory sites, staining runs, and individual slides may be verified quantitatively, efficiently and effectively utilizing algorithm-based, automated image analysis. To investigate this premise, we tested the consistency of IHC staining in 40 routinely processed (formalin-fixed, paraffin-embedded) human tissues using 10 common antibiomarker antibodies on 2 Dako Omnis instruments at 2 locations (Carpinteria, CA: 30 m above sea level and Longmont, CO: 1500 m above sea level) programmed with identical, default settings and sample pretreatments. Digital images of IHC-labeled sections produced by a whole slide scanner were analyzed by a simple commercially available algorithm and compared with a board-certified veterinary pathologist's semiquantitative scoring of staining intensity. The image analysis output correlated well with pathology scores but had increased sensitivity for discriminating subtle variations and providing reproducible digital quantification across sites as well as within and among staining runs at the same site. Taken together, our data indicate that digital image analysis offers an objective and quantifiable means of verifying IHC staining parameters as a part of laboratory quality assurance systems.