University of Veterinary Medicine Vienna Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness Purpose Accurate non-invasive prediction of histopathologic invasiveness and recurrence risk remains a clinical challenge in resectable non-small cell lung cancer (NSCLC). We developed and validated the Edge Proximity Score (EPS), a novel [18F]FDG PET/CT-based spatial imaging feature that quantifies the displacement of SUVmax relative to the tumor centroid and perimeter, to assess tumor aggressiveness and predict progression-free survival (PFS). Methods This retrospective study included 244 NSCLC patients with preoperative [18F]FDG PET/CT. EPS was computed from normalized SUVmax-to-centroid and SUVmax-to-perimeter distances. A total of 115 PET radiomics features were extracted and standardized. Eight machine learning models (80:20 split) were trained to predict lymphovascular invasion (LVI), visceral pleural invasion (VPI), and spread through air spaces (STAS), with feature importance assessed using SHAP. Prognostic analysis was conducted using multivariable Cox regression. A survival prediction model incorporating EPS was externally validated in the TCIA cohort. RNA sequencing data from 76 TCIA patients were used for transcriptomic and immune profiling. Results EPS was significantly elevated in tumors with LVI, VPI, and STAS (P < 0.001), consistently ranked among the top SHAP features, and was an independent predictor of PFS (HR = 2.667, P = 0.015). The EPS-based nomogram achieved AUCs of 0.67, 0.70, and 0.68 for predicting 1-, 3-, and 5-year PFS in the TCIA validation cohort. High EPS was associated with proliferative and metabolic gene signatures, whereas low EPS was linked to immune activation and neutrophil infiltration. Conclusion EPS is a biologically relevant, non-invasive imaging biomarker that may improve risk stratification in NSCLC. en TEXT journal article Wissenschaftlicher Artikel Articolo scientifico Text journal article Text Wissenschaftlicher Artikel Documento PDF Articolo scientifico NSCLC Tumor Invasiveness [18F]FDG PET/CT Radiomics 2026 2026 Zewen Jiang Clemens Spielvogel David Haberl Josef Yu Maximilian Krisch Szabolcs Szakall Peter Molnar Janos Fillinger Lilla Horvath Ferenc Renyi-Vamos Clemens Aigner Balazs Dome Christian Lang Zsolt Megyesfalvi Lukas Kenner Marcus Hacker Springer