Abstract 72: Unbiased AI detection of tertiary lymphoid structures from H&E whole-slide images using mRNA-derived labels predicts survival in NSCLC.

Abstract Tertiary lymphoid structures (TLS) are recognized prognostic markers in non-small cell lung cancer (NSCLC), yet manual detection from H HR 0.76; 95% CI 0.61-0.94; log-rank p = 0.014). Adjusting for age, sex, and histology, AI-predicted TLS enrichment remained independently associated with favorable OS (HR 0.77; 95% CI, 0.61-0.97; p = 0.025). Among the covariates, older age (greater than the cohort median) was associated with worse survival (HR 1.25; 95% CI, 1.01-1.55; p = 0.039), while sex and histologic subtype (LUSC vs LUAD) were not significant predictors (HR 1.12; 95% CI, 0.89-1.41; p = 0.337 and HR 0.99; 95% CI, 0.79-1.25; p = 0.957, respectively). These findings indicate that the model’s TLS-enrichment prediction captures clinically meaningful tumor microenvironment features that stratify OS beyond standard clinicopathologic factors. AI-assisted H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 72.