Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow

Stijn A van Bergeijk; Nikolas Stathonikos; Natalie D Ter Hoeve; Maxime W Lafarge; Tri Q Nguyen; Paul J van Diest; Mitko Veta

doi:10.1016/j.jpi.2023.100316

Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow

J Pathol Inform. 2023 May 4:14:100316. doi: 10.1016/j.jpi.2023.100316. eCollection 2023.

Authors

Stijn A van Bergeijk¹, Nikolas Stathonikos¹, Natalie D Ter Hoeve¹, Maxime W Lafarge^{2

3}, Tri Q Nguyen¹, Paul J van Diest¹, Mitko Veta²

Affiliations

¹ Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands.
² Medical Image Analysis Group (IMAG/e), Eindhoven University of Technology, Eindhoven, The Netherlands.
³ Computational and Translational Pathology Group, Department of Pathology and Molecular Pathology, University Hospital and University of Zürich, Schmelzbergstrasse 12, 8091 Zurich, Switzerland.

Abstract

Introduction: Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations.

Methods: Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ.

Results: MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R² 0.85 and 0.83, respectively), LM-MC and AI-MC (R² 0.85 and 0.95), and WSI-MC and AI-MC (R² 0.77 and 0.83). Agreement in BR between modalities was high in both biopsies and resections: LM-MC and WSI-MC (κ 0.93 and 0.83, respectively), LM-MC and AI-MC (κ 0.89 and 0.83), and WSI-MC and AI-MC (κ 0.96 and 0.73).

Conclusion: This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.

Keywords: Artificial intelligence; Bloom & Richardson grade; Breast cancer; Digital pathology; Light microscopy; Mitotic index.