AI-informed computational pathology classifier predicts outcomes across treatment modalities in muscle-invasive urothelial carcinoma

Urothelial carcinoma (UC) is one of the leading causes of cancer-related mortality, and effective, scalable biomarkers for treatment planning remain limited. We present UC-TIL, an artificial intelligence (AI)-based model that quantifies spatial patterns of tumor-infiltrating lymphocytes (TILs) from routine H&E-stained slides to predict survival and immunotherapy response. We analyzed 558 whole-slide images across three cohorts: TCGA (D₀&₁, N = 292), Emory (D₂, N = 161), and TRRC2819 (D₃, N = 105), spanning chemotherapy and immune checkpoint inhibitor (ICI) treatments. UC-TIL classification was associated with OS (HR = 2.11, 95 %CI:1.01-4.41, p = 0.011) and PFS (HR = 3.68, 95 %CI:1.07-12.65, p = 0.0012) in locally advanced disease (D₁ and D₂), with consistent results in metastatic disease (D₃) (HR = 1.73, 95 %CI:1.08-2.77, p = 0.043; PFS HR = 1.73, 95 %CI:1.07-2.81, p = 0.047). In the ICI-treated D₃ cohort, UC-TIL achieved AUC = 0.757 and identified non-responders with 91 % specificity. UC-TIL enables reliable risk stratification and treatment response prediction in both locally advanced and metastatic urothelial carcinoma by analyzing spatial TIL patterns from standard pathology slides. These findings position UC-TIL as a readily deployable tool to guide personalized therapy across multiple clinical settings.