AI-derived Histopathological Signatures Are Associated With Response to Mepolizumab in Chronic Rhinosinusitis With Nasal Polyps

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease with variable response to biologic therapy. Tissue-based predictors of treatment response are limited, and conventional eosinophil-focused histopathology has shown inconsistent predictive value. Objective: To assess whether artificial intelligence (AI)-derived baseline histopathology of nasal polyps contains information associated with clinical response to mepolizumab in CRSwNP. Methods: This hypothesis-generating substudy included patients with severe CRSwNP enrolled in a randomized controlled trial. Baseline biopsies were analysed using an AI-driven spatial histopathology pipeline extracting 84 morphological features per cell. Features were evaluated in eosinophil-only, noneosinophil, and all-cell configurations. Treatment response at 6 and 12 months was defined using EUFOREA criteria. Linear discriminant analysis and partial least squares regression were applied. Results: Fifty-eight patients were included. At 12 months, the eosinophil-only model showed limited discrimination (AUC ≈ 0.43), the noneosinophil model moderate discrimination (AUC ≈ 0.62), and the all-cell model the highest performance (AUC ≈ 0.75). At 6 months, all models showed limited discrimination (AUC ≈ 0.49). For continuous outcomes, noneosinophil models explained the greatest variance (R² up to ≈ 0.34). Conclusion: AI-derived baseline histopathology contains information associated with clinical response to mepolizumab in CRSwNP. Models incorporating the broader tissue microenvironment outperformed eosinophil-focused approaches, supporting the potential of computational pathology for pretreatment stratification of biologic therapy in CRSwNP.