Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Nicholas Lucarelli, Brandon Ginley, Jarcy Zee, Sayat Mimar, Anindya S. Paul, Sanjay Jain, Seung Seok Han, Luis Rodrigues, Tezcan Ozrazgat-Baslanti, Michelle L. Wong, Girish Nadkarni, William L. Clapp, Kuang Yu Jen, Pinaki Sarder

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background Reference histomorphometric data of healthy human kidneys are largely lacking because of laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning (DL), computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and eGFR in a multinational set of reference kidney tissue sections. Methods A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid–Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. Results Our DL model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of increasing age. Conclusions Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools can increase the efficiency and rigor of histomorphometric analysis.

Original languageEnglish
Pages (from-to)1726-1737
Number of pages12
JournalKidney360
Volume4
Issue number12
DOIs
StatePublished - Dec 1 2023

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