Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness.
Authors
Affiliations (15)
Affiliations (15)
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Vienna, 1090, Austria.
- Christian Doppler Laboratory of Applied Metabolomics, Medical University of Vienna, Vienna, Austria.
- Pozitron PET/CT Center, Budapest, Hungary.
- National Koranyi Institute of Pulmonology, Budapest, Hungary.
- Department of Thoracic Surgery, National Institute of Oncology-Semmelweis University, Budapest, Hungary.
- National Institute of Oncology and National Tumor Biology Laboratory, Budapest, Hungary.
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
- Department of Translational Medicine, Lund University, Lund, Sweden.
- Division of Pulmonology, Department of Medicine II, Medical University of Vienna, Vienna, Austria.
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria.
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, Vienna, Austria.
- Department of Molecular Biology, Umeå University, Umeå, Sweden.
- Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Vienna, 1090, Austria. [email protected].
- Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria. [email protected].
Abstract
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 [<sup>18</sup>F]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). This retrospective study included 244 NSCLC patients with preoperative [<sup>18</sup>F]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. 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. EPS is a biologically relevant, non-invasive imaging biomarker that may improve risk stratification in NSCLC.