Hebei General Hospital researchers develop a radiomics-based machine learning pipeline to preoperatively predict spread through air spaces (STAS) in lung adenocarcinoma. They segment tumor regions on CT images, extract quantitative texture, shape, and intensity features, and apply LASSO and classifiers including a ResNet50 deep learning network. The model achieves AUCs up to 0.918, offering a non-invasive tool to guide surgical planning.

Key points

• Extracted 3D CT radiomic features (texture, shape, intensity) screened via Mann–Whitney U, Spearman filtering, and LASSO reduction.
• Combined clinical markers (CEA level, FEV1/FVC) with radiomics in a nomogram achieving AUC 0.878 for STAS prediction.
• Employed ResNet50-based deep learning to derive 2D features, boosting classification AUC to 0.918 in machine learning models.

Why it matters: This AI-driven radiomics approach enables non-invasive, accurate preoperative risk stratification for lung adenocarcinoma, improving surgical planning and patient outcomes.