| Authors |
Santo BA, Segal BH, Tomaszewski JE, Mohammad I, Worral AM, Jain S, Visser MB,
Sarder P
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| Submitted By |
Pinaki Sarder on 5/12/2020 |
| Status |
Published |
| Journal |
Proceedings of SPIE--the International Society for Optical Engineering |
| Year |
2020 |
| Date Published |
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| Volume : Pages |
11320 : Not Specified |
| PubMed Reference |
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| Abstract |
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 non-lytic, 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.
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