A Computational Histology Artificial Intelligence Prognostic Biomarker in Non-Small Cell Lung Cancer Using the Cancer Genome Atlas

Background/Objectives: Outcomes in non–small cell lung cancer (NSCLC) remain heterogeneous, even within stage and molecular subtypes. We evaluated whether a Computational Histology Artificial Intelligence (CHAI) biomarker, derived solely from the diagnostic hematoxylin-and-eosin (H&E) whole-slide image (WSI), provides prognostic information independent of established clinicopathologic factors. Methods: Using The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) projects, 914 patients with an evaluable diagnostic digitized whole-slide images (WSI) and outcomes data were stratified by stage and histology and randomly split into a development set (30%, N = 270) and a held-out validation set (70%, N = 644). A CHAI histologic signature was developed and locked on the development set and applied without modification to the validation set. In development, image features were extracted from H&E-stained WSIs and used to train a Cox proportional hazards model to output a CHAI prognostic biomarker score and then dichotomized into high (CHAI positive (+))- and low (CHAI negative (−))-risk groups. The primary endpoint was overall survival (OS), and the secondary endpoint was progression-free interval (PFI). Associations were assessed by Kaplan–Meier and Cox proportional-hazards models and adjusted for traditional clinicopathologic and genomic variables. Results: A total of 644 patients were included in the validation cohort. Median age was 68 (IQR: 60–74), 388 (60%) were male, 317 (49%) had LUAD, 327 (51%) had LUSC, and 57 (9%) were never smokers. CHAI-positive patients had significantly worse OS than CHAI-negative patients (3-year OS: 53% vs. 69%; log-rank p < 0.001). The biomarker remained significantly associated with OS (HR 1.70, 95% CI 1.29–2.24, p < 0.001) and PFI (HR 1.39, 95% CI 1.05–1.84, p = 0.023) in multivariable analysis. The effect was consistent across both histologies, and a model combining the CHAI score with clinicopathologic variables was well-calibrated in validation. The biomarker was not associated with stage, age, sex, or histologic subtype, and its adjusted prognostic effect was unchanged after relevant genomic alterations were added to the model. CHAI-positive tumors were, however, enriched for KRAS mutations (21% vs. 11% wild-type, p adjusted = 0.005). Conclusions: An H&E-only CHAI NSCLC biomarker was developed and provided independent prognostic stratification in a held-out NSCLC validation cohort. Such a biomarker has the potential to refine risk stratification in NSCLC.