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

Author:

Vande Casteele Niels1,Leighton Jonathan A2,Pasha Shabana F2,Cusimano Frank2,Mookhoek Aart3,Hagen Catherine E4,Rosty Christophe56,Pai Reetesh K7,Pai Rish K8ORCID

Affiliation:

1. Department of Medicine, University of California San Diego, La Jolla, California, USA

2. Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, Scottsdale, Arizona, USA

3. Department of Pathology, VU Medical Center, Amsterdam, the Netherlands

4. Department of Pathology and Laboratory Medicine, Mayo Clinic Rochester, Rochester, Minnesota, USA

5. Envoi Specialist Pathologists, Brisbane, Queensland, Australia

6. Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia

7. Division of Gastroenterology, Hepatology, and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA

8. Department of Pathology and Laboratory Medicine, Mayo Clinic Arizona, Scottsdale, Arizona, USA

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Gastroenterology,Immunology and Allergy

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