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Automated H-Scoring in Muscle-Invasive Bladder Cancer IHC: An Internal Validation Study
Matthew Yap1, Ioana-Maria Mihai1,2, Maram Awadh A Alanazi1,3
1Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada.
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Background: Immunohistochemistry (IHC) plays a central role in subtyping of muscle-invasive bladder cancer (MIBC), yet conventional semi-quantitative scoring lacks objectivity and scalability. Automated digital pathology offers potential solutions, but requires robust, marker-specific validation against expert consensus scoring. Methods: We developed and internally validated an automated digital pathology pipeline for continuous IHC H-score quantification using QuPath (v0.6.0-arm64). Tissue microarrays (TMAs) were generated from transurethral resection of bladder tumor (TURBT) specimens from a cohort of patients with MIBC treated at Vancouver General Hospital. Cell detection was performed using StarDist (v0.9), followed by automated intensity-based H-score calculation for four epithelial IHC marker stains (CK14, CK20, CK5/6, and Uroplakin II). H-scoring was then restricted to tumor epithelium by object-level classification using a supervised tumor/non-tumor classifier trained on pathologist-reviewed annotations. Automated scores were compared with consensus scores from three blinded pathologists using Pearson correlation, linear regression, intraclass correlation coefficients (ICC), and Bland-Altman analysis. Results: Automated H-scores demonstrated strong agreement with pathologist consensus across all four markers. CK14 showed near-perfect agreement (ICC ≈ 0.99) with minimal bias and narrow limits of agreement. CK20 also showed high agreement (ICC ≈ 0.95). CK5/6 and Uroplakin II demonstrated slightly lower agreement (ICC ≈ 0.92 to 0.93) with mild proportional bias. Across markers, the automated pipeline preserved a broad H-score range, with range ratios of 0.96 to 0.99. Conclusions: This study establishes a robust, methods-forward pipeline for automated continuous IHC H-scoring in MIBC. The internally validated framework provides a scalable foundation for external cohort testing and future clinical outcome-associated biomarker analyses.
