Zewen Jiang Zewen Jiang David Haberl David Haberl Clemens Spielvogel Clemens Spielvogel Szabolcs Szakall Szabolcs Szakall Peter Molnar Peter Molnar Josef Yu Josef Yu Victor Lungu Victor Lungu Janos Fillinger Janos Fillinger Ferenc Renyi-Vamos Ferenc Renyi-Vamos Clemens Aigner Clemens Aigner Balazs Dome Balazs Dome Christian Lang Christian Lang Lukas Kenner Lukas Kenner Zsolt Megyesfalvi Zsolt Megyesfalvi Marcus Hacker Marcus Hacker Springer 2026 Abstract Lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC) is a critical prognostic marker linked to higher risks of metastasis and recurrence. This study aimed to develop a non-invasive predictive model using body composition features from 18F-FDG PET/CT imaging to assess LVI risk in early-stage NSCLC patients. Methods We retrospectively analyzed 248 patients, including 153 from Vienna (training cohort) and 95 from Budapest (validation cohort). Preoperative 18F-FDG PET/CT scans were used to assess tumor metabolic parameters, including standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as body composition features, including visceral, subcutaneous, and intermuscular adipose tissue, skeletal muscle at L1–L5. LASSO regression identified key body composition features, and a logistic regression-based nomogram was constructed and validated through ROC analysis, calibration, decision curve analysis, and survival analysis. Results LVI was present in 66/153 (43.1%) of Vienna and 39/95 (41.1%) of Budapest patients. The nomogram, developed using the Vienna training cohort, incorporating MTV, N stage, and body composition achieved an AUC of 0.839 and 0.790 in the Budapest validation cohort. Statistical tests confirmed that the nomogram significantly outperformed models based on either clinical (p = 7.92e-06) or imaging variables alone (p = 0.0474). Furthermore, LVI predicted by the nomogram was associated with significantly poorer 3-year recurrence-free and 5-year survival. Conclusion Integrating body composition with clinical and tumor metabolic features from PET/CT enables preoperative prediction of LVI in NSCLC, supporting improved risk stratification. PDFDocument eng 2026-07-15T08:10:34.300455435Z 2026 NSCLC Lymphovascular Invasion Body Composition 18F-FDG PET/CT 2032849 b application/pdf http://creativecommons.org/licenses/by/4.0/