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Abstract: TH-PO567

Machine-Learning-Quantified Lupus Nephritis Histological Features Correlate With NIH Activity and Chronicity Index Subscores

Session Information

  • Pathology and Lab Medicine
    November 03, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
    Abstract Time: 10:00 AM - 12:00 PM

Category: Pathology and Lab Medicine

  • 1700 Pathology and Lab Medicine


  • Moll, Solange, Universite de Geneve, Geneve, GE, Switzerland
  • Austin, Cary D., Genentech Inc, South San Francisco, California, United States
  • Toth, Balazs, Genentech Inc, South San Francisco, California, United States
  • Kos, Filip, PathAI, Boston, Massachusetts, United States
  • Hennek, Stephanie, PathAI, Boston, Massachusetts, United States
  • Jayson, Christina, PathAI, Boston, Massachusetts, United States
  • Baker, Brian Hartman, PathAI, Boston, Massachusetts, United States
  • Trotter, Benjamin, PathAI, Boston, Massachusetts, United States
  • Lincoln, Webster U., Genentech Inc, South San Francisco, California, United States
  • Brosnan-Cashman, Jacqueline A., PathAI, Boston, Massachusetts, United States
  • Schindler, Thomas, F Hoffmann-La Roche AG, Basel, Basel-Stadt, Switzerland
  • Resnick, Murray, PathAI, Boston, Massachusetts, United States
  • Chang, Anthony, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
  • Wapinski, Ilan, PathAI, Boston, Massachusetts, United States
  • Montalto, Michael, PathAI, Boston, Massachusetts, United States
  • Garg, Jay P., Genentech Inc, South San Francisco, California, United States
  • Tew, Wei, Genentech Inc, South San Francisco, California, United States
  • Prunotto, Marco, F Hoffmann-La Roche AG, Basel, Basel-Stadt, Switzerland

Histologic evaluation of renal biopsies is necessary for lupus nephritis (LN) diagnosis and treatment decisions; however, interobserver variability and poor quantitation limit the utility of histology-based metrics for precision medicine. To mitigate these challenges, we developed ML-based models to quantify histologic features in LN.


374 hematoxylin and eosin (H&E)-stained whole-slide images (WSI) of non-LN kidney and LN biopsies were obtained, mainly from a LN cohort at the University of Geneva and a clinical trial of obinutuzumab (OBZ) in proliferative LN (NCT02550652). WSI were split into training (286; 76%) and validation (88; 24%) sets. Expert pathologist annotations trained deep convolutional neural networks, yielding two distinct segmentation models covering anatomic regions and histopathological features. Model performance was tested on a held-out set of 94 WSI. For each model, 20-30 image frames were annotated by 3-5 pathologists to derive ground truth consensus. Whole slide predictions on 73 baseline cases from the OBZ trial were correlated to manual revised NIH LN activity and chronicity index (CI) subscores and kidney function metrics using Spearman method.


The model performed comparably to pathologists on both WSI and frames identifying tissue regions (e.g. cortex, F1model=0.78; F1pathologist=0.75) and tissue features (e.g. interstitial inflammation, F1model=0.68; F1pathologist=0.60). ML-quantified interstitial inflammation and sclerotic glomeruli regions correlated with the NIH activity index interstitial inflammation (r=0.638; p<0.0001) and CI glomerulosclerosis subscores (r=0.702; p<0.0001), respectively, as well as with eGFR, creatinine, and UPCR (r=0.32 - 0.47; p<0.01).


We developed ML models that quantify histologic features on LN H&E biopsies, revealing significant correlations with NIH disease index subscores and kidney function metrics. The findings demonstrate the feasibility of ML for quantifying LN histologic features. The utility of this approach in predicting treatment response is being evaluated.


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