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Increasingly, deep learning based approaches replace traditional image analysis algorithms. In general, image analysis can provide a more reproducible quantification of morphology of individual cells or relevant tissue components such as glands. Quantification of immunohistochemical staining (IHC) is one example where automated methods are already being incorporated with some success into clinical practice 2. Automated image analysis can help to extract measurements and features that are known to be relevant. Digital pathology devices or image analysis algorithms used in diagnostic reporting usually require regulatory approval such as FDA (USA) or CE IVD (Europe).ĭigital pathology and image analysis could ensure greater accuracy, reproducibility and standardisation of study inclusion criteria and outcomes. Such studies require high diagnostic standards and high reporting uniformity.ĭigital pathology refers to the use of computer workstations to view digital whole slide images (WSIs) obtained from high resolution scanning of glass microscope slides 1 uses include teaching, research or primary diagnostic reporting. Genomic approaches divide traditional entities into smaller subcategories requiring large multicentre, often multinational, interventional studies.
Stereology workshop 2018 trial#
The standardisation of digital image production, establishment of criteria for digital pathology use in pre‐clinical and clinical studies, establishment of performance criteria for image analysis algorithms and liaison with regulatory bodies to facilitate incorporation of image analysis applications into clinical practice are key issues to be addressed to improve digital pathology incorporation into clinical trials.Ĭellular pathology facilitates diagnosis and clinical trial treatment stratification. The impact of image analysis on quantitative tissue morphometrics in key areas such as standardisation of immunohistochemical stain interpretation, assessment of tumour cellularity prior to molecular analytical applications and the assessment of novel histological features is described. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. The current and emerging regulatory landscape is outlined.
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We reiterate the value of central review of pathology in clinical trials, and discuss inherent logistical, cost and performance advantages of using a digital approach. In this article, we provide an overview of the utility of such technologies in clinical trials and provide a discussion of the potential applications, current challenges, limitations and remaining unanswered questions that require addressing prior to routine adoption in such studies.
Stereology workshop 2018 manual#
Image analysis has great potential to identify, extract and quantify features in greater detail in comparison to pathologist assessment, which may produce improved prediction models or perform tasks beyond manual capability. Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology‐based trial entry criteria and endpoints, alongside extracting new insights from both existing and novel features.