Digital pathology in nephrology clinical trials, research, and pathology practice

作者：Barisoni Laura^*; Hodgin Jeffrey B

来源：Current Opinion in Nephrology and Hypertension, 2017, 26(6): 450-459.

DOI：10.1097/MNH.0000000000000360

• computational disease
• convolutional neural network
• deep learning
• focal segmental glomerulosclerosis
• morphometry
• nephrotic syndrome
• podocytes
• structural feature extraction

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.

• 出版日期2017-11