Arthritis Research & Therapy (Mar 2023)

Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation

  • Bella Mehta,
  • Susan Goodman,
  • Edward DiCarlo,
  • Deanna Jannat-Khah,
  • J. Alex B. Gibbons,
  • Miguel Otero,
  • Laura Donlin,
  • Tania Pannellini,
  • William H. Robinson,
  • Peter Sculco,
  • Mark Figgie,
  • Jose Rodriguez,
  • Jessica M. Kirschmann,
  • James Thompson,
  • David Slater,
  • Damon Frezza,
  • Zhenxing Xu,
  • Fei Wang,
  • Dana E. Orange

DOI
https://doi.org/10.1186/s13075-023-03008-8
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 13

Abstract

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Abstract Background We sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples. Methods We compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs. Results Synovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82. Conclusions H&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.

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