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Software-assisted decision support in digital histopathology.
The Journal of Pathology ( IF 7.3 ) Pub Date : 2020-02-25 , DOI: 10.1002/path.5388
Ralf Huss 1 , Sarah E Coupland 2
Affiliation  

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.

中文翻译:

数字组织病理学中的软件辅助决策支持。

组织诊断是病理学家的世界,并且它正变得越来越数字化,以利用个性化医学和对患者进行分层的巨大潜力,从而实现现代疗法的管理。因此,为了利用现代计算机技术的发展,病理学家的日常任务正在发生巨大变化,并且将变得越来越苛刻。病理学家的角色已经从仅描述疾病的形态和现象学迅速演变为成为新颖,最有效的治疗选择的看门人。基于从组织或一组细胞以及相关的元数据中检索和管理各种复杂信息,这是可能的。智能和自学软件解决方案可以支持和指导病理学家根据多个目标分子的准确和鲁棒定量对临床相关的决策进行评分,或替代生物标记物作为伴随或补充诊断方法,以及相关的空间关系和来自数字H&E和多路图像的背景信息。借助在一张幻灯片上提供的多种染色技术,高分辨率图像分析工具以及高端计算机硬件,机器和深度学习解决方案现在提供了诊断规则集和算法,在经过精心设计的研究中仍需要临床验证。在进入临床实践之前,“人为因素”病理学家需要对来自“计算病理学数字黑匣子”的输出产生信任,包括图像分析解决方案和人工智能算法来支持关键的临床决策,否则这些决策将无法实现。©2020英国和爱尔兰病理学会。由John Wiley&Sons,Ltd.出版
更新日期:2020-02-25
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