Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies

• Type of Material: Article
• Authors: Yijiang Chen, Case Western Reserve University; Jarcy Zee, University of Pennsylvania; Andrew Janowczyk, Emory University; Jeremy Rubin, University of Pennsylvania; Paula Toro, Cleveland Clinic Foundation; Kyle J Lafata, Duke University; Laura H Mariani, University of Michigan; Lawrence B Holzman, University of Pennsylvania; Jeffrey B Hodgin, University of Michigan; Anant Madabhushi, Emory University; Laura Barisoni, Duke University
• Language: English
• Date: 2023-05-01
• Publisher: Wolters KluwerHealth, Inc
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.34067/KID.0000000000000116
• Title of Journal or Parent Work: Kidney360
• Volume: 4
• Issue: 5
• Start Page: 648
• End Page: 658
• Grant/Funding Information: Research reported in this study was supported by the National Institute of Diabetes and Digestive and Kidney Disease of the National Institutes of Health under the award number 2R01DK118431-04 and the Nephcure foundation.; Research reported in this publication was supported by the National Cancer Institute under award numbers R01CA268287A1, U01CA269181, R01CA26820701A1, R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160), and the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly, and Astrazeneca.
• Keywords: Kidney; Biopsy; Capillaries; Clinical Relevance; Kidney Transplantation
• Research Categories: Biology, Biostatistics; Health Sciences, Pathology; Health Sciences, Oncology; Health Sciences, Epidemiology; Engineering, Biomedical

Abstract

BackgroundThe association between peritubular capillary (PTC) density and disease progression has been studied in a variety of kidney diseases using immunohistochemistry. However, other PTC attributes, such as PTC shape, have not been explored yet. The recent development of computer vision techniques provides the opportunity for the quantification of PTC attributes using conventional stains and whole-slide images.MethodsTo explore the relationship between PTC characteristics and clinical outcome, n=280 periodic acid-Schiff-stained kidney biopsies (88 minimal change disease, 109 focal segmental glomerulosclerosis, 46 membranous nephropathy, and 37 IgA nephropathy) from the Nephrotic Syndrome Study Network digital pathology repository were computationally analyzed. A previously validated deep learning model was applied to segment cortical PTCs. Average PTC aspect ratio (PTC major to minor axis ratio), size (PTC pixels per PTC segmentation), and density (PTC pixels per unit cortical area) were computed for each biopsy. Cox proportional hazards models were used to assess associations between these PTC parameters and outcome (40% eGFR decline or kidney failure). Cortical PTC characteristics and interstitial fractional space PTC density were compared between areas of interstitial fibrosis and tubular atrophy (IFTA) and areas without IFTA.ResultsWhen normalized PTC aspect ratio was below 0.6, a 0.1, increase in normalized PTC aspect ratio was significantly associated with disease progression, with a hazard ratio (95% confidence interval) of 1.28 (1.04 to 1.59) (P = 0.019), while PTC density and size were not significantly associated with outcome. Interstitial fractional space PTC density was lower in areas of IFTA compared with non-IFTA areas.ConclusionsComputational image analysis enables quantification of the status of the kidney microvasculature and the discovery of a previously unrecognized PTC biomarker (aspect ratio) of clinical outcome.