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Abstract <p>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 <sup>18</sup>F-FDG PET/CT imaging to assess LVI risk in early-stage NSCLC patients.</p> Methods <p>We retrospectively analyzed 248 patients, including 153 from Vienna (training cohort) and 95 from Budapest (validation cohort). Preoperative <sup>18</sup>F-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.</p> Results <p>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 (<i>p</i> = 7.92e-06) or imaging variables alone (<i>p</i> = 0.0474). Furthermore, LVI predicted by the nomogram was associated with significantly poorer 3-year recurrence-free and 5-year survival.</p> Conclusion <p>Integrating body composition with clinical and tumor metabolic features from PET/CT enables preoperative prediction of LVI in NSCLC, supporting improved risk stratification.</p> Graphical abstract <p></p>

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Using 18F-FDG PET/CT-derived body composition features to predict lymphovascular invasion in non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Zewen Jiang,
  • David Haberl,
  • Clemens Spielvogel,
  • Szabolcs Szakall,
  • Peter Molnar,
  • Josef Yu,
  • Victor Lungu,
  • Janos Fillinger,
  • Ferenc Renyi-Vamos,
  • Clemens Aigner,
  • Balazs Dome,
  • Christian Lang,
  • Lukas Kenner,
  • Zsolt Megyesfalvi,
  • Marcus Hacker

摘要

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.

Graphical abstract