Abstract

Utilizing Deep Learning to Analyze Whole Slide Images of Colonic Biopsies for Associations Between Eosinophil Density and Clinicopathologic Features in Active Ulcerative Colitis

Inflamm Bowel Dis. 2021 Jun 9;izab122. doi: 10.1093/ibd/izab122. Online ahead of print.

Niels Vande Casteele 1, Jonathan A Leighton 2, Shabana F Pasha 2, Frank Cusimano 2, Aart Mookhoek 3, Catherine E Hagen 4, Christophe Rosty 5 6, Reetesh K Pai 7, Rish K Pai 8

 
     

Author information

  • 1Department of Medicine, University of California San Diego, La Jolla, California, USA.
  • 2Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, Scottsdale, Arizona, USA.
  • 3Department of Pathology, VU Medical Center, Amsterdam, the Netherlands.
  • 4Department of Pathology and Laboratory Medicine, Mayo Clinic Rochester, Rochester, Minnesota, USA.
  • 5Envoi Specialist Pathologists, Brisbane, Queensland, Australia.
  • 6Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
  • 7Division of Gastroenterology, Hepatology, and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
  • 8Department of Pathology and Laboratory Medicine, Mayo Clinic Arizona, Scottsdale, Arizona, USA.

Abstract

Background: Eosinophils have been implicated in the pathogenesis of ulcerative colitis and have been associated with disease course and therapeutic response. However, associations between eosinophil density, histologic activity, and clinical features have not been rigorously studied.

Methods: A deep learning algorithm was trained to identify eosinophils in colonic biopsies and validated against pathologists' interpretations. The algorithm was applied to sigmoid colon biopsies from a cross-sectional cohort of 88 ulcerative colitis patients with histologically active disease as measured by the Geboes score and Robarts histopathology index (RHI). Associations between eosinophil density, histologic activity, and clinical features were determined.

Results: The eosinophil deep learning algorithm demonstrated almost perfect agreement with manual eosinophil counts determined by 4 pathologists (interclass correlation coefficients: 0.805-0.917). Eosinophil density varied widely across patients (median 113.5 cells per mm2, interquartile range 108.9). There was no association between eosinophil density and RHI (P = 0.5). Significant differences in eosinophil density were seen between patients with Montreal E3 vs E2 disease (146.2 cells per mm2 vs 88.2 cells per mm2, P = 0.005). Patients on corticosteroids had significantly lower eosinophil density (62.9 cells per mm2 vs 124.1 cells per mm2, P = 0.006). No association between eosinophil density and biologic use was observed (P = 0.5).

Conclusions: We developed a deep learning algorithm to quantify eosinophils in colonic biopsies. Eosinophil density did not correlate with histologic activity but did correlate with disease extent and corticosteroid use. Future studies applying this algorithm in larger cohorts with longitudinal follow-up are needed to further elucidate the role of eosinophils in ulcerative colitis.

 

 

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