6024 Background: Tertiary lymphoid structures (TLS) are recognized prognostic markers in Head and Neck Squamous Cell Carcinoma (HNSCC), yet manual assessment from H these labels were used to train a foundation model-based AI using Imagene’s OI Suite powered by CanvOI with a 3:1 train–test split. Survival analyses were performed at the patient level in 443 evaluable patients with high-quality H 95% CI 0.53–0.98; log-rank P = 0.039). After adjustment for age, sex, and stage, AI-predicted TLS enrichment remained independently associated with improved overall survival (HR, 0.73; 95% CI, 0.54–1.00; P = 0.0499). These findings suggest that the AI model successfully translates molecular TLS signatures into histological predictors, capturing critical tumor microenvironment (TME) features that refine risk stratification beyond standard clinicopathologic factors. Conclusions: AI-based H&E WSI analysis helps identify TLS enrichment as a potential predictor of overall survival in HNSCC. This unbiased computational approach provides a reproducible and objective methodology using H&E slides alone, without the need for additional molecular or immunohistochemical assays, thereby supporting immune risk stratification for precision immunotherapy, particularly in resource-limited settings.
Lim et al. (Wed,) studied this question.