Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology. With an aim to support the UK’s efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities. The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics. Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a “must-have” for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce. Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.

Healthcare is increasingly and routinely generating large volumes of data from different sources, which are difficult to handle and integrate. Confidence in data can be established through the knowledge that the data are validated, well-curated and with minimal bias or errors. As the National Measurement Institute of the UK, the National Physical Laboratory (NPL) is running an interdisciplinary project on digital health data curation. The project addresses one of the key challenges of the UK’s Measurement Strategy, to provide confidence in the intelligent and effective use of data. A workshop was organised by NPL in which important stakeholders from NHS, industry and academia outlined the current and future challenges in healthcare data curation. This paper summarises the findings of the workshop and outlines NPL’s views on how a metrological approach to the curation of healthcare data sets could help solve some of the important and emerging challenges of utilising healthcare data.

Achieving the highest levels of compliance with the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically, while several ontologies to capture the metrological information are available, practical implementation examples are few. This work aims to close this gap by discussing how standardised measurement data and metadata could be presented using semantic web technologies. The examples provided in this paper are machine-actionable descriptions of Earth observation and bathymetry measurement datasets, based on two ontologies of quantities and units of measurement selected for their prominence in the semantic web. The selected ontologies demonstrated a good coverage of the concepts related to quantities, dimensions, and individual units as well as systems of units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations such as standard uncertainty, expanded uncertainty, combined uncertainty, coverage factor, probability distribution, etc. These results highlight the need for both (I) user-friendly tools for semantic representations of measurement datasets and (II) the establishment of good practices within each scientific community. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.

Background.: Preimplantation biopsy combines measurements of injury into a composite index to inform organ acceptance. The uncertainty in these measurements remains poorly characterized, raising concerns variability may contribute to inappropriate clinical decisions. Methods.: We adopted a metrological approach to evaluate biopsy score reliability. Variability was assessed by performing repeat biopsies (n = 293) on discarded allografts (n = 16) using 3 methods (core, punch, and wedge). Uncertainty was quantified using a bootstrapping analysis. Observer effects were controlled by semi-blinded scoring, and the findings were validated by comparison with standard glass evaluation. Results.: The surgical method strongly determined the size (core biopsy area 9.04 mm², wedge 37.9 mm²) and, therefore, yield (glomerular yield r = 0.94, arterial r = 0.62) of each biopsy. Core biopsies yielded inadequate slides most frequently. Repeat biopsy of the same kidney led to marked variation in biopsy scores. In 10 of 16 cases, scores were contradictory, crossing at least 1 decision boundary (ie, to transplant or to discard). Bootstrapping demonstrated significant uncertainty associated with single-slide assessment; however, scores were similar for paired kidneys from the same donor. Conclusions.: Our investigation highlights the risks of relying on single-slide assessment to quantify organ injury. Biopsy evaluation is subject to uncertainty, meaning each slide is better conceptualized as providing an estimate of the kidney’s condition rather than a definitive result. Pooling multiple assessments could improve the reliability of biopsy analysis, enhancing confidence. Where histological quantification is necessary, clinicians should seek to develop new protocols using more tissue and consider automated methods to assist pathologists in delivering analysis within clinical time frames.

Optimisation of imaging protocols is essential to maximise the use of image-guided radiotherapy. This article evaluates the time for daily online imaging with TomoTherapy® (Accuray®, Sunnyvale, CA), separating mechanical scan acquisition from radiographer-led image matching, to estimate the time required for a clinical research study (VoxTox). Over 5 years, 18 533 treatments were recorded for 3 tumour sites of interest (prostate, head and neck and central nervous system). Data were collected for scan length, number of CT slices, slice thickness, scan acquisition time and image matching time. The proportion of coarse thickness scans increased over time, with a move of making coarse scans as the default. There was a strong correlation between scan time and scan length. Scan acquisition requires 40 s of processing time. For coarse scans, each additional centimetre requires 8 s for acquisition. Image matching takes approximately 1.5 times as long, so each additional centimetre needs 20 s extra in total. Modest changes to the imaging protocol have minimal impact over the course of the day. This work quantified the effect of changes to clinical protocols required for research. The results have been found to be reassuring in the busy National Institutes of Health department. This novel method of data collection and analysis provides evidence of the minimal impact of research on clinical turnover. Whilst the data relate specifically to TomoTherapy, some aspects may apply to other platforms in the future.

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.