https://phaidra.vetmeduni.ac.at/o:5344 Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness Jiang, Zewen Zewen Jiang 0009-0005-0172-868X Spielvogel, Clemens Clemens Spielvogel Haberl, David David Haberl Yu, Josef Josef Yu Krisch, Maximilian Maximilian Krisch Szakall, Szabolcs Szabolcs Szakall Molnar, Peter Peter Molnar Fillinger, Janos Janos Fillinger Horvath, Lilla Lilla Horvath Renyi-Vamos, Ferenc Ferenc Renyi-Vamos Aigner, Clemens Clemens Aigner Dome, Balazs Balazs Dome Lang, Christian Christian Lang Megyesfalvi, Zsolt Zsolt Megyesfalvi Kenner, Lukas Lukas Kenner 0000-0003-2184-1338 University of Veterinary Medicine Vienna Hacker, Marcus Marcus Hacker 0000-0002-4222-4083 Springer journal article open access eng 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. NSCLC Tumor Invasiveness [18F]FDG PET/CT Radiomics http://creativecommons.org/licenses/by/4.0/ https://phaidra.vetmeduni.ac.at/api/object/o:5344/download 10.1007/s00259-025-07528-0 https://phaidra.vetmeduni.ac.at/o:605 application/pdf European Journal of Nuclear Medicine and Molecular Imaging issn:1619-7070 VoR European Journal of Nuclear Medicine and Molecular Imaging 53 3 1.65 MB 2026