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.
Neutrophil extracellular trap (NET) formation is an alternate immunologic weapon used mainly by neutrophils. Chromatin
backbones fused with proteins derived from granules are shot like projectiles onto foreign invaders. It is thought that this
mechanism is highly anti-microbial, aids in preventing bacterial dissemination, is used to break down structures several
sizes larger than neutrophils themselves, and may have several more uses yet unknown. NETs have been implied to be
involved in a wide array of systemic host immune defenses, including sepsis, autoimmune diseases, and cancer. Existing
methods used to visually quantify NETotic versus non-NETotic shapes are extremely time-consuming and subject to user
bias. These limitations are obstacles to developing NETs as prognostic biomarkers and therapeutic targets. We propose an
automated pipeline for quantitatively detecting neutrophil and NET shapes captured using a flow cytometry-imaging
system. Our method uses contrast limited adaptive histogram equalization to improve signal intensity in dimly illuminated
NETs. From the contrast improved image, fixed value thresholding is applied to convert the image to binary. Feature
extraction is performed on the resulting binary image, by calculating region properties of the resulting foreground
structures. Classification of the resulting features is performed using Support Vector Machine. Our method classifies NETs
from neutrophils without traps at 0.97/0.96 sensitivity/specificity on n = 387 images, and is 1500X faster than manual
classification, per sample. Our method can be extended to rapidly analyze whole-slide immunofluorescence tissue images
for NET classification, and has potential to streamline the quantification of NETs for patients with diseases associated with
cancer and autoimmunity.
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