Neutrophil Extracellular Traps (NETs): An unexplored territory in renal pathobiology, a pilot computational study.
Neutrophil Extracellular Traps (NETs): An unexplored territory in renal pathobiology, a pilot computational study.
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中性粒细胞胞外陷阱（NET）：肾脏病理学中一个未探索的领域，一项试点计算研究。
DOI：
10.1117/12.2549340
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sarder,Pinaki
中科院分区：
文献类型：
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作者：
Santo,BrianaA;Segal,BrahmH;Tomaszewski,JohnE;Mohammad,Imtiaz;Worral,AmberM;Jain,Sanjay;Visser,MichelleB;Sarder,Pinaki
在现代医学和人工智能时代，图像分析和机器学习已经彻底改变了诊断病理学，促进了计算机辅助诊断（CAD）的发展，从而规避了普遍的诊断挑战。虽然CAD将加快和提高临床工作流程的精度，但其预后潜力，当与临床结局数据配对时，仍然不确定。在高影响性肾脏疾病中，如糖尿病肾病和狼疮性肾炎（LN），由于短暂疾病状态中结构变化的微妙性，进展通常迅速发生并且没有立即检测到。在这种状态下，探索可量化的图像生物标志物，如神经细胞外陷阱（NET），可以揭示与临床数据相关的替代进展指标。NET作为免疫原性细胞结构参与LN，其发生和失调导致过度的组织损伤和病变表现。我们建议，肾活检NET分布将作为一个歧视，预测LN的生物标志物，并将补充现有的分类方案。我们已经开发了一个计算流水线分割网样结构在LN活检。从我们的活检切片中分割出的NET样结构值得进一步研究，因为它们在病理学上是不同的，并且类似于非溶解性的、重要的NET。相应的H&E区域的检查主要将NET样结构置于肾小球中，包括全局和节段硬化的肾小球和小管腔。我们的工作通过以下方式继续探索LN活检中的NET样结构：根据不断发展的NET定义修订检测和分析方法，以及2.）分类NET形态学，以实现组织病理学图像中类NET结构的监督分类。
In the age of modern medicine and artificial intelligence, image analysis and machine learning have revolutionized diagnostic pathology, facilitating the development of computer aided diagnostics (CADs) which circumvent prevalent diagnostic challenges. Although CADs will expedite and improve the precision of clinical workflow, their prognostic potential, when paired with clinical outcome data, remains indeterminate. In high impact renal diseases, such as diabetic nephropathy and lupus nephritis (LN), progression often occurs rapidly and without immediate detection, due to the subtlety of structural changes in transient disease states. In such states, exploration of quantifiable image biomarkers, such as Neutrophil Extracellular Traps (NETs), may reveal alternative progression measures which correlate with clinical data. NETs have been implicated in LN as immunogenic cellular structures, whose occurrence and dysregulation results in excessive tissue damage and lesion manifestation. We propose that renal biopsy NET distribution will function as a discriminate, predictive biomarker in LN, and will supplement existing classification schemes. We have developed a computational pipeline for segmenting NET-like structures in LN biopsies. NET-like structures segmented from our biopsies warrant further study as they appear pathologically distinct, and resemble nonlytic, vital NETs. Examination of corresponding H&E regions predominantly placed NET-like structures in glomeruli, including globally and segmentally sclerosed glomeruli, and tubule lumina. Our work continues to explore NET-like structures in LN biopsies by: 1.) revising detection and analytical methods based on evolving NETs definitions, and 2.) cataloguing NET morphology in order to implement supervised classification of NET-like structures in histopathology images.