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Background <p>This study aimed to develop and validate a novel lymph node staging system integrating anatomical location and quantitative characteristics, evaluate its prognostic prediction efficacy in non-small-cell lung cancer (NSCLC), and establish a multivariate prognostic model.</p> Methods <p>A total of 23,676 patients with NSCLC from the SEER database (2010–2015) were enrolled. Optimal cutoffs for lymph node parameters (NPLN, LNR, LODDS) were determined using X-tile software. Composite variables (N-NPLN, N-LNR, N-LODDS) were constructed by integrating N staging. Independent prognostic factors were screened via Cox regression, and a nomogram was developed. Performance was assessed using the receiver operating characteristic curves, calibration curves, and decision curve analysis.</p> Results <p>N-LODDS staging demonstrated optimal prognostic prediction, significantly outperforming N-LNR and N-NPLN. The nomogram incorporating N-LODDS, tumor size, and nine independent prognostic factors showed superior discrimination and calibration (5&#xa0;year area under the curve 0.740; 95% confidence interval 0.731–0.749) in both training and validation cohorts, with significant advantages over the TNM staging system (all <i>P</i>&lt;0.001).</p> Conclusion <p>The N-LODDS staging system significantly improves prognostic accuracy by integrating anatomical and quantitative lymph node features, providing a novel tool for personalized NSCLC management. Future multicenter prospective studies are needed to validate its clinical utility.</p>

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N-LODDS: A Novel Integrated Lymph Node Staging System Enhancing Prognostic Accuracy in Non-Small-Cell Lung Cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Qiying Chen,
  • Meihong Yao,
  • Zishan Chen,
  • Shiwen Liu,
  • Jinman Zhuang,
  • Xi Chen,
  • Jie Yi,
  • Binghua Tu,
  • Ziyue Yang,
  • Yinghong Yang,
  • Fei He

摘要

Background

This study aimed to develop and validate a novel lymph node staging system integrating anatomical location and quantitative characteristics, evaluate its prognostic prediction efficacy in non-small-cell lung cancer (NSCLC), and establish a multivariate prognostic model.

Methods

A total of 23,676 patients with NSCLC from the SEER database (2010–2015) were enrolled. Optimal cutoffs for lymph node parameters (NPLN, LNR, LODDS) were determined using X-tile software. Composite variables (N-NPLN, N-LNR, N-LODDS) were constructed by integrating N staging. Independent prognostic factors were screened via Cox regression, and a nomogram was developed. Performance was assessed using the receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

N-LODDS staging demonstrated optimal prognostic prediction, significantly outperforming N-LNR and N-NPLN. The nomogram incorporating N-LODDS, tumor size, and nine independent prognostic factors showed superior discrimination and calibration (5 year area under the curve 0.740; 95% confidence interval 0.731–0.749) in both training and validation cohorts, with significant advantages over the TNM staging system (all P<0.001).

Conclusion

The N-LODDS staging system significantly improves prognostic accuracy by integrating anatomical and quantitative lymph node features, providing a novel tool for personalized NSCLC management. Future multicenter prospective studies are needed to validate its clinical utility.