Abstract 1458: Deep learning of H&E slides adds prognostic value beyond IASLC grading in non-mucinous lung adenocarcinoma among never-smokers.

Abstract Background: Lung cancer in never smokers (LCINS) most often presents as non-mucinous adenocarcinoma. The International Association for the Study of Lung Cancer (IASLC) system is the current standard for histologic grading, but its use is limited by interobserver variability, time-intensive manual assessment, and limited scalability. We evaluated whether a deep learning model applied to routine hematoxylin and eosin (H 55 deaths occurred by 5 years and 102 deaths by 10 years from diagnosis, out of 190 events overall. Data were split into training (n=409), internal cross-validation (n=45), and held-out validation (n=141) sets. A convolutional neural network generated continuous patient-level risk scores from H (2) baseline + deep learning; (3) baseline + IASLC grade + deep learning. Results: Deep learning yielded higher AUCs than IASLC grade for 5 year (0.84 0.76-0.92 vs 0.70 0.62-0.78; p=0.01) and 10 years overall survival (0.75 0.61-0.90 vs 0.64 0.48-0.79; p=0.59). In multivariable analyses, at 5 years the AUCs were 0.79 for baseline + IASLC, 0.86 for baseline + deep learning (p0.01 vs baseline + IASLC), and 0.87 for the full model (better than simpler models, p=0.04); at 10 years the corresponding AUCs were 0.82, 0.86, and 0.87, with no statistically significant differences overall (p=0.63), possibly reflecting fewer late events. In a Cox model for 5 years of follow-up the deep-learning low-risk group had improved overall survival versus the high-risk group (HR 0.31, 95% CI 0.13-0.76). Conclusions: Deep learning on routine H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1458.