Deep Learning-Based Virtual Elastin Staining Improves Visceral Pleural Invasion Assessment in Lung Cancer.

Abstract

Accurate assessment of visceral pleural invasion is essential for staging and prognostication in non-small cell lung cancer, yet distinguishing elastin-rich pleural layers on routine hematoxylin and eosin (H&E) sections remains a diagnostic challenge. To overcome the cost and workflow delays associated with special elastic stains, we developed a deep-learning pipeline that generates a virtual elastin stain, termed synthetic eosin-based elastin fluorescence, directly from standard brightfield H&E slides. A key innovation of this study was the use of intrinsic eosin fluorescence from the same H&E section to create a perfectly coregistered, high-fidelity ground truth for training a conditional generative adversarial network, eliminating the spatial mismatches common in multislide approaches. In a multi-institutional validation, supplementing H&E review with synthetic eosin-based elastin fluorescence significantly improved pathologists' diagnostic accuracy for visceral pleural invasion compared with H&E alone (P < .0001). Notably, the preanalytical factors that optimized model performance, including thinner tissue sections (1-3 μm) and high-resolution scanning, also enhanced the perceptual contrast of elastin for pathologists, demonstrating a strong synergy between computational and conventional diagnostic optimization. This study establishes and validates a robust framework for high-fidelity virtual staining that improves diagnostic accuracy and provides a scalable pathway for integrating deep learning-based tools into routine digital pathology. The proposed approach offers a practical and cost-effective alternative to ancillary special stains in non-small cell lung cancer evaluation.