Computational Pathology of Proteinuric Diseases

Project Summary/Abstract: Computational Pathology for Proteinuric Glomerulopathies
The presently employed morphology-based classification system of focal segmental glomerulosclerosis (FSGS)
and minimal change disease (MCD) does not adequately capture the clinical and molecular heterogeneity of
these diseases and impairs the ability of clinicians to precisely define a patient’s disease, or predict outcome or
effective intervention. The goal of this research is to advance the work of the glomerular disease research
community by identifying biologically-relevant surrogates and subclasses of FSGS/MCD using computational
pathology and machine learning methods. Prospective, longitudinal, multi-dimensional data sets that include
digital kidney biopsies and molecular and clinical information can be analyzed by advanced “computer vision”
methods and machine learning analytical approaches. This project will employ these rich resources and
methods, which offer an unprecedented opportunity to leverage information derived from kidney tissue to
improve the diagnosis, outcome prediction, and identification of glomerular disease mechanisms.
The interdisciplinary team assembled to conduct this study has vast experience and a long-standing history of
collaboration. In our preliminary studies we have demonstrated that (i) structural changes associate with
outcomes and molecular mechanisms and improve clinical outcome prediction beyond current clinical
approaches, and that (ii) computer vision technology can be used to accurately and efficiently detect normal and
abnormal kidney structures and quantify textural (i.e., the spatial relationship between pixel values) and
morphological (i.e., shape, size) information from kidney tissue.
We will test our central hypothesis that inherent in the complexity of the structural changes in the renal
parenchyma is information predictive of underlying disease biology and clinical outcome. We will pursue this
hypothesis (1) by testing the clinical and molecular relevance of automatic detection and quantification of known
morphologic biomarkers of clinical outcomes and mechanisms and of groups of patients with similar DL-derived
morphologic characteristics; (2) by extracting next-generation pathomic features from DL-derived morphologic
parameters and by testing their associations—individually and combined into pathomic profiles—with clinical
outcomes and gene expression; and (3) by building machine learning-based models that integrate computer
vision-derived pathology data with gene expression and clinical data to predict individual patient clinical
outcomes, and to assess the additive prediction value of each data domain. Finally, we will group patients using
the biomarkers identified to be most predictive of clinical outcomes. Ultimately, our work will contribute to a
foundation for the deployment of a comprehensive artificial intelligence-guided precision medicine program for
FSGS/MCD, applying descriptive, predictive, and ultimately prescriptive analytics as support tools for practicing
pathologists and nephrologists.

Grant Number: 5R01DK118431-08
NIH Institute/Center: NIH
Principal Investigator: Laura Barisoni