Nuffield Department of Surgical Sciences, University of Oxford, and Oxford NIHR Biomedical Research Centre, Oxford, UK.
Institute of Cancer Sciences - Pathology, University of Glasgow, Glasgow, UK.
J Pathol Clin Res. 2019 Apr;5(2):81-90. doi: 10.1002/cjp2.127. Epub 2019 Mar 25.
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. 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. 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. 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. The current and emerging regulatory landscape is outlined. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. 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 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.
数字病理学和图像分析有可能提高基于病理学的试验纳入标准和终点的准确性、可重复性和标准化,同时从现有和新特征中提取新的见解。与病理学家评估相比,图像分析具有在更大程度上识别、提取和量化特征的巨大潜力,这可能会产生改进的预测模型或执行超出手动能力的任务。在本文中,我们概述了这些技术在临床试验中的实用性,并讨论了潜在的应用、当前的挑战、局限性和在常规采用这些研究之前需要解决的未回答的问题。我们重申了在临床试验中进行病理中心审查的价值,并讨论了使用数字方法的固有逻辑、成本和性能优势。概述了当前和新兴的监管格局。讨论了数字平台和远程学习在提高临床试验病理学家的培训和绩效方面的作用。描述了图像分析对定量组织形态计量学的影响,例如免疫组织化学染色解释的标准化、分子分析应用前肿瘤细胞密度的评估以及新的组织学特征的评估。解决数字图像制作的标准化、建立临床前和临床研究中数字病理学使用的标准、建立图像分析算法的性能标准以及与监管机构联系以促进图像分析应用纳入临床实践是提高数字病理学纳入临床试验的关键问题。