Probabilistic modelling of diabetic nephropathy progression

Samuel Border; Kuang-Yu Jen; Washington LC dos-Santos; John Tomaszewski; Pinaki Sarder

doi:10.1117/12.2549171

16 March 2020 Probabilistic modelling of diabetic nephropathy progression

Samuel Border, Kuang-Yu Jen, Washington LC dos-Santos, John Tomaszewski, Pinaki Sarder

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 1132014 (2020) https://doi.org/10.1117/12.2549171
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

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Samuel Border, Kuang-Yu Jen, Washington LC dos-Santos, John Tomaszewski, and Pinaki Sarder "Probabilistic modelling of diabetic nephropathy progression", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132014 (16 March 2020); https://doi.org/10.1117/12.2549171

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KEYWORDS

Kidney

Biopsy

Image segmentation

Data modeling

Feature extraction

Modeling

Particle filters

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