Probabilistic modeling of Diabetic Nephropathy progression

Samuel Border, Kuang Yu Jen, Washington L.C. dos-Santos, John Tomaszewski, Pinaki Sarder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 semi-qualitative 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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510634077
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Digital Pathology - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11320
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Digital Pathology
Country/TerritoryUnited States
CityHouston
Period2/19/202/20/20

Keywords

  • Bayesian network
  • Diabetic nephropathy
  • Minimum Spanning Trees (MST)
  • Probabilistic graphical modelling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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