Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Journal: Scientific reports
Published Date: Aug 5, 2023

Abstract

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

Authors

  • Joonsang Lee
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Elisa Warner
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Salma Shaikhouni
    Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA.
  • Markus Bitzer
    Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA.
  • Matthias Kretzler
    Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA. Electronic address: kretzler@umich.edu.
  • Debbie Gipson
    Department of Pediatrics, Pediatric Nephrology, University of Michigan, Ann Arbor, MI, USA.
  • Subramaniam Pennathur
    Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA.
  • Keith Bellovich
    Department of Internal Medicine, Nephrology, St. Clair Nephrology Research, Detroit, MI, USA.
  • Zeenat Bhat
    Department of Internal Medicine, Nephrology, Wayne State University, Detroit, MI, USA.
  • Crystal Gadegbeku
    Department of Internal Medicine, Nephrology, Cleveland Clinic, Cleveland, OH, USA.
  • Susan Massengill
    Department of Pediatrics, Pediatric Nephrology, Levine Children's Hospital, Charlotte, NC, USA.
  • Kalyani Perumal
    Department of Internal Medicine, Nephrology, Department of JH Stroger Hospital, Chicago, IL, USA.
  • Jharna Saha
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Yingbao Yang
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Jinghui Luo
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Laura Mariani
    Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA.
  • Jeffrey B Hodgin
    Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
  • Arvind Rao
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.

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