Probabilistic modeling of Diabetic Nephropathy progression.
Probabilistic modeling of Diabetic Nephropathy progression.
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糖尿病肾病进展的概率模型。
DOI：
10.1117/12.2549171
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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：
Border,Samuel;Jen,Kuang-Yu;Dos-Santos,WashingtonLc;Tomaszewski,John;Sarder,Pinaki
根据病理学家的观察，糖尿病肾病 (DN) 的进展分为几个阶段，具有不同水平的蛋白尿、白蛋白尿和身体特征。这些物理变化主要在患者的肾小球内可见，肾小球充当血液返回氧合的过滤单元。随着DN分期的增加，可以观察到肾小球基底膜增厚、系膜扩张以及结节性硬化的发展。病理学家对 DN 不同阶段的分类是基于对个体肾小球这些特征的半定性评估。能够基于易于观察和隐藏的图像特征的组合来概率地推断单个肾小球的阶段成员资格将是进一步了解 DN 进展驱动因素的宝贵工具。 R 的 bnlearn 包中包含的马尔可夫粒子过滤器用于查询使用结构爬山算法对一组肾小球特征构建的贝叶斯网络 (BN)。这些特征既包括肾小球面积和系膜核数量等传统特征，也包括源自最小生成树（MST）以量化系膜核空间分布的更抽象特征。我们使用来自多个机构的图像得出的结果表明，这些抽象特征在疾病进展过程中对 DN 分期成员产生不同的影响。结合临床数据的进一步研究将为肾脏病学家提供 DN 患者中存在的定量因素的“白盒”可视化。
Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient’s glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semiqualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a “white box” visual of quantitative factors present in DN patients.