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/.

Ultrasound-triggered therapeutic microbubbles enhance the efficacy of cytotoxic drugs by increasing circulation and tumor drug accumulation and limiting bioavailability and toxicity in normal tissues.

Most cancer patients receive chemotherapy at some stage of their treatment which makes improving the efficacy of cytotoxic drugs an ongoing and important goal. Despite large numbers of potent anti-cancer agents being developed, a major obstacle to clinical translation remains the inability to deliver therapeutic doses to a tumor without causing intolerable side effects. To address this problem, there has been intense interest in nanoformulations and targeted delivery to improve cancer outcomes. The aim of this work was to demonstrate how vascular endothelial growth factor receptor 2 (VEGFR2)-targeted, ultrasound-triggered delivery with therapeutic microbubbles (thMBs) could improve the therapeutic range of cytotoxic drugs. Methods: Using a microfluidic microbubble production platform, we generated thMBs comprising VEGFR2-targeted microbubbles with attached liposomal payloads for localised ultrasound-triggered delivery of irinotecan and SN38 in mouse models of colorectal cancer. Intravenous injection into tumor-bearing mice was used to examine targeting efficiency and tumor pharmacodynamics. High-frequency ultrasound and bioluminescent imaging were used to visualise microbubbles in real-time. Tandem mass spectrometry (LC-MS/MS) was used to quantitate intratumoral drug delivery and tissue biodistribution. Finally, 89Zr PET radiotracing was used to compare biodistribution and tumor accumulation of ultrasound-triggered SN38 thMBs with VEGFR2-targeted SN38 liposomes alone. Results: ThMBs specifically bound VEGFR2 in vitro and significantly improved tumor responses to low dose irinotecan and SN38 in human colorectal cancer xenografts. An ultrasound trigger was essential to achieve the selective effects of thMBs as without it, thMBs failed to extend intratumoral drug delivery or demonstrate enhanced tumor responses. Sensitive LC-MS/MS quantification of drugs and their metabolites demonstrated that thMBs extended drug exposure in tumors but limited exposure in healthy tissues, not exposed to ultrasound, by persistent encapsulation of drug prior to elimination. 89Zr PET radiotracing showed that the percentage injected dose in tumors achieved with thMBs was twice that of VEGFR2-targeted SN38 liposomes alone. Conclusions: thMBs provide a generic platform for the targeted, ultrasound-triggered delivery of cytotoxic drugs by enhancing tumor responses to low dose drug delivery via combined effects on circulation, tumor drug accumulation and exposure and altered metabolism in normal tissues.

In Vitro Engineering of High Modulus Cartilage-Like Constructs.

To date, the outcomes of cartilage repair have been inconsistent and have frequently yielded mechanically inferior fibrocartilage, thereby increasing the chances of damage recurrence. Implantation of constructs with biochemical composition and mechanical properties comparable to natural cartilage could be advantageous for long-term repair. This study attempted to create such constructs, in vitro, using tissue engineering principles. Bovine synoviocytes were seeded on nonwoven polyethylene terephthalate fiber scaffolds and cultured in chondrogenic medium for 4 weeks, after which uniaxial compressive loading was applied using an in-house bioreactor for 1 h per day, at a frequency of 1 Hz, for a further 84 days. The initial loading conditions, determined from the mechanical properties of the immature constructs after 4 weeks in chondrogenic culture, were strains ranging between 13% and 23%. After 56 days (sustained at 84 days) of loading, the constructs were stained homogenously with Alcian blue and for type-II collagen. Dynamic compressive moduli were comparable to the high end values for native cartilage and proportional to Alcian blue staining intensity. We suggest that these high moduli values were attributable to the bioreactor setup, which caused the loading regime to change as the constructs developed, that is, the applied stress and strain increased with construct thickness and stiffness, providing continued sufficient cell stimulation as further matrix was deposited. Constructs containing cartilage-like matrix with response to load similar to that of native cartilage could produce long-term effective cartilage repair when implanted.

Prospector: A web-based tool for rapid acquisition of gold standard data for pathology research and image analysis.

BACKGROUND: Obtaining ground truth for pathological images is essential for various experiments, especially for training and testing image analysis algorithms. However, obtaining pathologist input is often difficult, time consuming and expensive. This leads to algorithms being over-fitted to small datasets, and inappropriate validation, which causes poor performance on real world data. There is a great need to gather data from pathologists in a simple and efficient manner, in order to maximise the amount of data obtained. METHODS: We present a lightweight, web-based HTML5 system for administering and participating in data collection experiments. The system is designed for rapid input with minimal effort, and can be accessed from anywhere in the world with a reliable internet connection. RESULTS: We present two case studies that use the system to assess how limitations on fields of view affect pathologist agreement, and to what extent poorly stained slides affect judgement. In both cases, the system collects pathologist scores at a rate of less than two seconds per image. CONCLUSIONS: The system has multiple potential applications in pathology and other domains.

RandomSpot: A web-based tool for systematic random sampling of virtual slides.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications.