Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation. Academic Article uri icon

Overview

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.

authors

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

publication date

  • March 2, 2023

Research

keywords

  • Arthritis, Rheumatoid
  • Osteoarthritis

Identity

PubMed Central ID

  • PMC9979511

Scopus Document Identifier

  • 85149324093

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0154515

PubMed ID

  • 36864474

Additional Document Info

volume

  • 25

issue

  • 1