Titre
Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.
Type
article
Institution
Externe
Périodique
Auteur(s)
Chen, Y.
Auteure/Auteur
Zee, J.
Auteure/Auteur
Janowczyk, A.R.
Auteure/Auteur
Rubin, J.
Auteure/Auteur
Toro, P.
Auteure/Auteur
Lafata, K.J.
Auteure/Auteur
Mariani, L.H.
Auteure/Auteur
Holzman, L.B.
Auteure/Auteur
Hodgin, J.B.
Auteure/Auteur
Madabhushi, A.
Auteure/Auteur
Barisoni, L.
Auteure/Auteur
Liens vers les personnes
ISSN
2641-7650
Statut éditorial
Publié
Date de publication
2023-05-01
Volume
4
Numéro
5
Première page
648
Dernière page/numéro d’article
658
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
Publication Status: ppublish
Publication Status: ppublish
Résumé
Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when measured in interstitial fractional space. PTC shape (aspect ratio) is associated with clinical outcome in glomerular diseases.
The 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.
To 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.
When 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.
Computational 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.
The 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.
To 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.
When 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.
Computational 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.
PID Serval
serval:BIB_20EC4F95E882
PMID
Open Access
Oui
Date de création
2023-04-11T08:15:10.012Z
Date de création dans IRIS
2025-05-20T17:26:02Z