肾脏病临床试验、研究和病理实践中的数字病理学。
Digital pathology in nephrology clinical trials, research, and pathology practice.
机构信息
aDepartment of Pathology, University of Miami, Miller School of Medicine, Miami, Florida, USA bDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
出版信息
Curr Opin Nephrol Hypertens. 2017 Nov;26(6):450-459. doi: 10.1097/MNH.0000000000000360.
PURPOSE OF REVIEW
In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine.
RECENT FINDINGS
Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases.
SUMMARY
The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.
目的综述
在这篇综述中，我们将讨论：（i）最近在数字技术和计算工程方面的进展如何应用于临床研究、试验和实践中的肾脏病学；（ii）新数字环境的优势；（iii）认识到其挑战如何为变革提供机会；以及（iv）肾脏病学在即将到来的肾脏精准和预测医学时代。
最近的发现
最近的研究强调了新的标准化方案如何促进数字病理学数据库基础设施以及形态学、形态计量学和计算机辅助定量分析的协调。数字病理学为临床试验和研究提供了强大的方案，有可能识别以前未被充分利用或未被认识到的具有临床意义的参数。数字病理学与分子特征的结合正在为建立具有临床相关性的肾脏疾病形态组学分类奠定基础。
总结
数字病理学在临床研究和试验中的引入，以及现代软件生态系统的逐步实施，为开发新的预测诊断范式和计算机辅助算法提供了机会，将肾脏疾病的实践转变为现代计算科学。
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