Work

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

Year: 2025

Type: article

Abstract: 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 (D 0 & 1 , N=292), Emory (D 2 , N=161), and TRRC2819 (D 3 , 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 (D 1 and D 2 ), with consistent results in metastatic disease (D 3 ) (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 D 3 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. • UC-TIL is an AI model that quantifies spatial TIL patterns from H&E slides. • Predicts overall survival and immunotherapy response in urothelial carcinoma. • Validated across 146 internal and 412 external cases from TCGA, Emory, and TRRC cohorts. • Outperformed manual TIL scoring by expert pathologists and identified ICI non-responders with high specificity. • Provides a scalable and interpretable tool for personalized bladder cancer management.

Source: Cancer Letters

DOI: 10.1016/j.canlet.2025.218059