Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany.
Department of Cellular and Molecular Pathology, University of Liverpool, Liverpool, UK.
J Pathol. 2020 Apr;250(5):685-692. doi: 10.1002/path.5388. Epub 2020 Feb 25.
Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta-data. Intelligent and self-learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high-resolution image analysis tools, and high-end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well-designed studies. Before entering the clinical practice, the 'human factor' pathologist needs to develop trust in the output coming from the 'digital black box of computational pathology', including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
组织诊断学是病理学家的领域，它正日益数字化，以利用个性化医学和分层患者的巨大潜力，实现现代疗法的管理。因此，病理学家的日常任务发生了巨大变化，为了利用现代计算机技术的发展，任务将变得越来越繁重。病理学家的角色已经迅速从仅仅描述疾病的形态和现象学，转变为成为新的和最有效的治疗选择的守门员。这是基于从组织或一组细胞中检索和管理广泛的复杂信息以及相关的元数据来实现的。智能和自学习软件解决方案可以支持和指导病理学家根据对多个目标分子或替代生物标志物的准确和稳健的定量进行基于临床的相关决策，这些标志物作为伴随或补充诊断，以及来自数字 H&E 和多重化图像的相关空间关系和上下文信息。随着单张幻灯片上的多重染色技术、高分辨率图像分析工具和高端计算机硬件的出现，机器和深度学习解决方案现在提供了诊断规则集和算法，这些规则集和算法仍需要在精心设计的研究中进行临床验证。在进入临床实践之前，病理学家需要对来自“计算病理学的数字黑匣子”的输出建立信任，包括图像分析解决方案和人工智能算法，以支持否则无法获得的关键临床决策。©2020 大不列颠及爱尔兰病理学会。由 John Wiley & Sons, Ltd. 出版。
