Deep learning-powered virtual eosin-based elastic fiber framework imaging improves objective grading and prognostic stratification of lung adenocarcinoma

Histological grading of lung adenocarcinoma is limited by interobserver variability, particularly when distinguishing noninvasive lepidic growth from invasive patterns. This distinction depends on evaluation of the alveolar elastic fiber framework, which is often insufficiently visualized on routine hematoxylin and eosin (H&E) slides. We developed a deep learning framework to improve grading objectivity by computationally visualizing elastic fiber–related architecture from standard H&E images. A dual-stream EfficientNet-B0 classifier was trained using spatially coregistered H&E and eosin-based elastin fluorescence (EBEF) images. To enable clinical deployment without additional staining or fluorescence microscopy, a Pix2Pix-HD generative adversarial network was trained to synthesize high-fidelity virtual EBEF images from H&E alone. The resulting automated pipeline classified histological patterns, quantified pattern proportions, and assigned tumor grades. Performance was evaluated on independent multicenter cohorts, with ablation analyses assessing key pipeline components. Prognostic relevance was explored using a curated TCGA lung adenocarcinoma cohort. The benchmark dual-stream model using real H&E and EBEF achieved 89.3% tile-level classification accuracy. Using virtual EBEF images, the automated grading pipeline achieved 94.2% accuracy on an external validation cohort, outperforming an H&E-only model (79.6%). Ablation studies confirmed that performance gains depended on informative multimodal input, accurate spatial registration, and attentive feature fusion. AI-assigned grades demonstrated a consistent trend toward association with overall survival in TCGA, with significant stratification observed when low-grade tumors were combined (log-rank p = 0.048). This framework enables objective grading of lung adenocarcinoma from a single routine H&E slide by enhancing visualization of elastic fiber–related architecture. The approach improves grading consistency and prognostic stratification without altering standard pathology workflows.