Computational Pathology for Lupus Nephritis
Abstract
Nearly one third of all adults with Systemic Lupus Erythematosus (SLE) present with lupus nephritis (LN) at diagnosis and up to two thirds manifest kidney involvement during the course of the disease with higher prevalence in minority populations particularly those of African ancestry. Furthermore, as many as 10 percent of patients with LN will progress to end-stage and require dialysis and/or kidney transplant. The ability to accurately identify LN patients at risk for progression could shift much of the current management paradigm from treatment to prevention. However, the prognostic significance of histopathologic classification of LN, the most current arising from a collaboration between the International Society of Nephrology and the Renal Pathology Society (ISN/RPS) in 2004 is controversial. Therefore, novel approaches are required to obtain continuous, quantitative data to improve accuracy, reproducibility, and prognostic utility. Digital pathology, a dynamic, image-based environment for the acquisition, management, and analysis of information generated from digitized images, is emerging in the setting of clinical trials and research, including kidney disease, but has yet to be applied to lupus nephritis biopsy interpretation in a large cohort. We hypothesize that digital pathology and image analysis approaches will improve the prognostic utility of the kidney biopsy in lupus nephritis and allow more efficacious treatment approaches. In this project we will apply detailed quantitative morphologic scoring, computer-aided, semi-automated, morphometric analysis with deep learning segmentation, and integrative molecular approaches to establish improved methods of kidney biopsy interpretation in lupus nephritis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2021
- Accession Number
- AD1162383
Entities
People
- Arvind Rao
- Jeffrey B. Hodgin
Organizations
- University of Michigan