Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment

Yijiang Chen; Bangchen Wang; Dawit Demeke; Fan Fan; Celine Berthier; Laura Mariani; Kyle Lafata; Lawrence Holzman; Jeffrey Hodgin; Andrew Janowczyk; Laura Barisoni; Anant Madabhushi

doi:10.2215/CJN.0000000597

Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment

Clin J Am Soc Nephrol. 2024 Dec 23. doi: 10.2215/CJN.0000000597. Online ahead of print.

Authors

Yijiang Chen^{1

2}, Bangchen Wang³, Dawit Demeke⁴, Fan Fan^{2

5}, Celine Berthier⁶, Laura Mariani⁶, Kyle Lafata^{3

7

8

9}, Lawrence Holzman¹⁰, Jeffrey Hodgin⁴, Andrew Janowczyk^{5

11

12}, Laura Barisoni³, Anant Madabhushi^{5

13}

Affiliations

¹ Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.
² Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
³ Department of Pathology, Duke University, Durham, NC, United States.
⁴ Department of Pathology, University of Michigan, Ann Arbor, MI, United States.
⁵ Department of Biomedical engineering, Emory University, Atlanta, GA, USA.
⁶ Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, United States.
⁷ Department of Radiology, Duke University, Durham, North Carolina, USA.
⁸ Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
⁹ Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
¹⁰ Department of Internal Medicine, Division of Nephrology, University of Pennsylvania, Philadelphia, PA, USA.
¹¹ Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland.
¹² Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland.
¹³ Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.

PMID: 39714939
DOI: 10.2215/CJN.0000000597

Abstract

Background: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of disease progression and assess the clinical relevance of the interplay between a variety of PTC characteristics and their microenvironment in glomerular diseases.

Methods: A total of 344 NEPTUNE/CureGN participants were included: 112 minimal change disease, 134 focal segmental glomerulosclerosis, 61 membranous nephropathy, and 37 IgA nephropathy. A PAS-stained whole slide image per patient was manually segmented for cortex, pre- and mature IFTA. Interstitial fractional space (IFS) was computationally quantified. A deep-learning model was applied to segment PTCs. Spatial and shape PTC pathomic features (230) were extracted from the cortex, IFTA, and non-IFTA sub-regions. Participants were divided into training and testing datasets (1:1). Univariate models incorporating IFTA subregions, and IFS-PTC density were constructed. LASSO regression models were used to identify the top PTC features associated with disease progression stratified by IFTA and non-IFTA sub-regions. Machine learning models were built using the top PTC features in IFTA and non-IFTA sub-regions to prognosticate disease progression.

Results: PTC density in pre+mature IFTA and IFS, shape features in pre+mature IFTA, and spatial architecture features in the non-IFTA regions associated with disease progression. The machine learning generated risk scores showed a significant association with disease progression on the independent testing set.

Conclusion: We uncovered previously underrecognized digital biomarkers of disease progression and the clinical relevance of the complex interplay between the status of the PTCs and the interstitial microenvironment.