Deep Learning Tubulometrics to Stratify Tubular Injury in Whole-Slide Kidney Specimens

Background: Acute kidney injury is highly prevalent and most often involves tubular injury. However, studies on detailed assessment of tubular epithelial cell morphometry within tubuli are missing. To this end, we provide a deep learning-enhanced method to segment and analyze tubular epithelial cells and tubuli ("tubulometrics") in whole slide kidney specimens of wild-type mice and mice models of acute and chronic tubular injury. Methods: Kidneys were embedded in paraffin and stained with PAS and immunohistochemistry before digital scanning. More than 1200 tubuli and 6800 tubular epithelial cells of control mice, mice suffering ischemia-reperfusion injury or chronic tubular injury were manually annotated. Subsequently, a deep learning algorithm was trained to segment tubuli and tubular epithelial cells and morphometric readouts were obtained. Furthermore, tubulometrics were compared to traditional tubular injury scores. Results: Tubular area, tubular cell number and density per tubulus section were identified as morphometric readouts. These tubulometrics were able to complement a traditional tubular injury score (PAS score) and interstitial fibrosis tubular atrophy score (IFTA). In wild-type mice PAS score was 0, IFTA 0, mean tubular area 2339 [µm2], mean tubular cell count per tubulus 7.27, and mean tubular cell density 3.09 [10-3/µm2]. In mice with ischemia-reperfusion injury PAS score was 3.44, IFTA 10%, mean tubular area 1818 [µm2], mean tubular cell count per tubulus 2.02, mean tubular cell density 1.11 [10-3/µm2]. In mice with crystal-induced chronic tubular injury PAS score was 3.06, IFTA 40%, mean tubular area 2746 [µm2], mean tubular cell count per tubulus 7.71, and mean tubular cell density 2.81 [10-3/µm2]. The deep learning segmentation algorithm ensured time-efficiency and scalability by taking 5 minutes per slide. Conclusion: We propose the analysis of tubular epithelial cell morphometry in kidney specimens to better identify and stratify tubular injury. Whereas tubular epithelial cell loss was found in acute injury, tubular hypertrophy together with cell regeneration were observed in the chronic model. Integrated with existing histopathological scores a more detailed assessment of kidney biopsies might become possible. Further studies are needed to validate the findings in larger cohorts. Funding: Government Support – Non-U.S.

Authors

Institutions

Publication Details

Journal
Journal of the American Society of Nephrology
Published
2024-10-01
DOI
https://doi.org/10.1681/asn.20247g5d1wbh
Primary Topic
Chronic Kidney Disease and Diabetes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep Learning Tubulometrics to Stratify Tubular Injury in Whole-Slide Kidney Specimens

Martin Klaus, Louisa Ehreiser, Peter Gaßmann, Stefanie Steiger et al.
Journal of the American Society of Nephrology
Chronic Kidney Disease and Diabetes
article

Deep Learning Tubulometrics to Stratify Tubular Injury in Whole-Slide Kidney Specimens

Martin Klaus, Louisa Ehreiser, Peter Gaßmann, Stefanie Steiger, Julia Lichtnekert, Maciej Lech, Hao Long, Hans‐Joachim Anders, Rosch Ronny Abdullah
article en

Abstract

Background: Acute kidney injury is highly prevalent and most often involves tubular injury. However, studies on detailed assessment of tubular epithelial cell morphometry within tubuli are missing. To this end, we provide a deep learning-enhanced method to segment and analyze tubular epithelial cells and tubuli ("tubulometrics") in whole slide kidney specimens of wild-type mice and mice models of acute and chronic tubular injury. Methods: Kidneys were embedded in paraffin and stained with PAS and immunohistochemistry before digital scanning. More than 1200 tubuli and 6800 tubular epithelial cells of control mice, mice suffering ischemia-reperfusion injury or chronic tubular injury were manually annotated. Subsequently, a deep learning algorithm was trained to segment tubuli and tubular epithelial cells and morphometric readouts were obtained. Furthermore, tubulometrics were compared to traditional tubular injury scores. Results: Tubular area, tubular cell number and density per tubulus section were identified as morphometric readouts. These tubulometrics were able to complement a traditional tubular injury score (PAS score) and interstitial fibrosis tubular atrophy score (IFTA). In wild-type mice PAS score was 0, IFTA 0, mean tubular area 2339 [µm2], mean tubular cell count per tubulus 7.27, and mean tubular cell density 3.09 [10-3/µm2]. In mice with ischemia-reperfusion injury PAS score was 3.44, IFTA 10%, mean tubular area 1818 [µm2], mean tubular cell count per tubulus 2.02, mean tubular cell density 1.11 [10-3/µm2]. In mice with crystal-induced chronic tubular injury PAS score was 3.06, IFTA 40%, mean tubular area 2746 [µm2], mean tubular cell count per tubulus 7.71, and mean tubular cell density 2.81 [10-3/µm2]. The deep learning segmentation algorithm ensured time-efficiency and scalability by taking 5 minutes per slide. Conclusion: We propose the analysis of tubular epithelial cell morphometry in kidney specimens to better identify and stratify tubular injury. Whereas tubular epithelial cell loss was found in acute injury, tubular hypertrophy together with cell regeneration were observed in the chronic model. Integrated with existing histopathological scores a more detailed assessment of kidney biopsies might become possible. Further studies are needed to validate the findings in larger cohorts. Funding: Government Support – Non-U.S.

Journal of the American Society of NephrologyVol. 35(10S)
Ludwig-Maximilians-Universität München (DE)
Zero hunger
Openalex Percentile: Top 73%
Chronic Kidney Disease and Diabetes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.