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April 5, 2026Cancer Research0 citations

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

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AWAnthony J. WongJKJessica KimSYS. Yoon

Key Points

  • The study aims to develop an AI model for detecting tertiary lymphoid structures (TLS) from H&E whole-slide images to predict survival in NSCLC.
  • Analyzed TCGA NSCLC mRNA expression data to compute enrichment scores for immune cells.
  • Calculated TLS enrichment scores from B cells, T cells, and dendritic cells in tumor samples.
  • Trained a machine learning model for TLS detection using labeled quartiles from enrichment scores.
  • Performed univariable and multivariable Cox regression analysis to evaluate survival associations.
  • The AI model achieved a training AUC of 0.84 and a test AUC of 0.92 for TLS detection.
  • Patients in the TLS-enriched group showed improved overall survival (HR 0.76; log-rank p = 0.014).
  • AI-predicted TLS enrichment was associated with favorable overall survival even after adjusting for age, sex, and histology (HR 0.77; p = 0.025).
  • Older age negatively impacted survival outcomes (HR 1.25; p = 0.039).

Abstract

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.

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Cite This Study

Wong et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd73a79560c99a0a376bhttps://doi.org/10.1158/1538-7445.am2026-72
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