Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system.

BACKGROUND: Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation. METHODS: The stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels. RESULTS: Stain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98-0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period. CONCLUSION: The proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.

The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis.

Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in detail, the results from a previously published algorithm automating the generation of tumor:stroma ratio (TSR) in colorectal clinical trial datasets. One dataset consisting of 2,211 cases and 106,268 expert-labelled images is used to identify quality issues, by visually inspecting cases where algorithm-pathologist agreement is lowest. Twelve categories are identified and used to analyze pathologist-algorithm agreement in relation to these categories. Of the 2,211 cases, 701 were found to be free from any image quality issues. Algorithm performance was then assessed, comparing pathologist agreement with image quality classification. It was found that agreement was lowest on poorly differentiated tissue, with a mean TSR difference of 0.25 (sd = 0.24). Removing images that contained quality issues increased accuracy from 80% to 83%, at the expense of reducing the dataset to 33,736 images (32%). Training the algorithm on the optimized dataset, prior to testing on all images saw a decrease in accuracy of 4%, indicating that the optimized dataset did not contain enough variation to generate a fully representative model. The results provide an in-depth perspective on image quality, highlighting the importance of the effects on downstream image analysis.

A Point-of-Use Quality Assurance Tool for Digital Pathology Remote Working.

Pathology services are facing pressures due to the COVID-19 pandemic. Digital pathology has the capability to meet some of these unprecedented challenges by allowing remote diagnoses to be made at home, during periods of social distancing or self-isolation. However, while digital pathology allows diagnoses to be made on standard computer screens, unregulated home environments may not be conducive for optimal viewing conditions. There is also a paucity of experimental evidence available to support the minimum display requirements for digital pathology. This study presents a Point-of-Use Quality Assurance (POUQA) tool for remote assessment of viewing conditions for reporting digital pathology slides. The tool is a psychophysical test combining previous work from successfully implemented quality assurance tools in both pathology and radiology to provide a minimally intrusive display screen validation task, before viewing digital slides. The test is specific to pathology assessment in that it requires visual discrimination between colors derived from hematoxylin and eosin staining, with a perceptual difference of ±1 delta E (dE). This tool evaluates the transfer of a 1 dE signal through the digital image display chain, including the observers' contrast and color responses within the test color range. The web-based system has been rapidly developed and deployed as a response to the COVID-19 pandemic and may be used by anyone in the world to help optimize flexible working conditions at: http://www. virtualpathology.leeds.ac.uk/res earch/systems/pouqa/.