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Objective <p>Accurate staging of primary lung cancer is crucial for optimizing therapeutic strategies but remains challenging in clinical practice. We aimed to develop a nomogram incorporating clinical characteristics with CT and PET findings to predict lymph node metastasis (LNM) in primary lung cancer.</p> Materials and methods <p>We retrospectively analyzed patients with primary lung cancer and mediastinal and hilar LNs from a tertiary care cancer center. All patients underwent endobronchial ultrasound-guided transbronchial needle aspiration, with diagnostic chest CT and PET-CT. Cytological confirmation of transbronchial needle aspiration samples served as the gold standard for diagnosing LNM. We employed an LN-level modeling approach and constructed five models for independent prediction of LNM: (1) Clinical-CT-PET model, (2) Clinical-CT model, (3) PET model, (4) Clinical-PET model, and (5) CT-PET model. Their performance was further evaluated in the subgroup of LNs &lt; 1 cm.</p> Results <p>This study included 455 patients (mean age 70 ± 10 years; 55.4% male), predominantly adenocarcinoma (62.0%). Most (68.1%) were stage III–IV. In total, 1391 lymph nodes (1112 training, 279 testing) were analyzed to develop and validate the nomogram. The Clinical-CT-PET model achieved the best diagnostic performance, with AUCs of 0.883 (training cohort) and 0.877 (test cohort), sensitivities of 79.5% and 80.0%, and specificities of 87.1% and 86.9%, respectively. For small LNs, it showed higher AUC (0.797 vs. 0.722, <i>p</i> &lt; 0.001) and sensitivity (71.4% vs. 52%) compared to the PET model.</p> Conclusion <p>We developed a nomogram that noninvasively estimates the risk of LNM in lung cancer that may inform individualized preoperative assessment and evidence-based decision-making.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can a nomogram integrating clinical, CT, and PET features improve preoperative prediction of lymph node metastasis in primary lung cancer, particularly in small nodes?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>We developed a Clinical-CT-PET nomogram that achieved the best diagnostic accuracy (AUCs 0.883 and 0.877) among five models, especially for small lymph nodes (&lt; 1 cm).</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>This noninvasive Clinical-CT-PET nomogram may improve the accuracy of preoperative lymph node staging and guide individualized treatment planning. It may also help avoid unnecessary invasive procedures in lung cancer patients, pending further multi-center validation.</i></p> Graphical Abstract <p></p>

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Development of PET/CT-clinical nomograms for predicting lymph node metastasis in primary lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Xiaoyu Han,
  • Wentao Li,
  • Julie Lin,
  • Shuoyang Li,
  • Maliazurina B. Saad,
  • Yuliya Kitsel,
  • Simon Heeke,
  • Lingzhi Hong,
  • Mohamed Qayati Mohamed,
  • Xiuning Le,
  • Natalie Vokes,
  • Myrna CB Godoy,
  • Brett W. Carter,
  • Girish S. Shroff,
  • George Eapen,
  • Lauren A. Byers,
  • Ara A. Vaporciyan,
  • Don L. Gibbons,
  • John Heymach,
  • Carol C. Wu,
  • Jianjun Zhang,
  • Jia Wu

摘要

Objective

Accurate staging of primary lung cancer is crucial for optimizing therapeutic strategies but remains challenging in clinical practice. We aimed to develop a nomogram incorporating clinical characteristics with CT and PET findings to predict lymph node metastasis (LNM) in primary lung cancer.

Materials and methods

We retrospectively analyzed patients with primary lung cancer and mediastinal and hilar LNs from a tertiary care cancer center. All patients underwent endobronchial ultrasound-guided transbronchial needle aspiration, with diagnostic chest CT and PET-CT. Cytological confirmation of transbronchial needle aspiration samples served as the gold standard for diagnosing LNM. We employed an LN-level modeling approach and constructed five models for independent prediction of LNM: (1) Clinical-CT-PET model, (2) Clinical-CT model, (3) PET model, (4) Clinical-PET model, and (5) CT-PET model. Their performance was further evaluated in the subgroup of LNs < 1 cm.

Results

This study included 455 patients (mean age 70 ± 10 years; 55.4% male), predominantly adenocarcinoma (62.0%). Most (68.1%) were stage III–IV. In total, 1391 lymph nodes (1112 training, 279 testing) were analyzed to develop and validate the nomogram. The Clinical-CT-PET model achieved the best diagnostic performance, with AUCs of 0.883 (training cohort) and 0.877 (test cohort), sensitivities of 79.5% and 80.0%, and specificities of 87.1% and 86.9%, respectively. For small LNs, it showed higher AUC (0.797 vs. 0.722, p < 0.001) and sensitivity (71.4% vs. 52%) compared to the PET model.

Conclusion

We developed a nomogram that noninvasively estimates the risk of LNM in lung cancer that may inform individualized preoperative assessment and evidence-based decision-making.

Key Points

Question Can a nomogram integrating clinical, CT, and PET features improve preoperative prediction of lymph node metastasis in primary lung cancer, particularly in small nodes?

Findings We developed a Clinical-CT-PET nomogram that achieved the best diagnostic accuracy (AUCs 0.883 and 0.877) among five models, especially for small lymph nodes (< 1 cm).

Clinical relevance This noninvasive Clinical-CT-PET nomogram may improve the accuracy of preoperative lymph node staging and guide individualized treatment planning. It may also help avoid unnecessary invasive procedures in lung cancer patients, pending further multi-center validation.

Graphical Abstract