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Objectives <p>This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may benefit from surgery in terms of survival.</p> Methods <p>We extracted information on mNSCLC-NET patients from the SEER database. Propensity score matching was used to eliminate bias between surgery and non-surgery groups. The effect of PTR on prognosis was assessed via Kaplan‒Meier analysis with the log-rank test and the Cox proportional hazards model. Feature selection was performed via the Boruta algorithm. Model building utilized fivefold cross-validation and applied five machine learning algorithms. The optimal model was selected and used to construct a visual network nomogram.</p> Results <p>Among the 1,776 eligible patients, 12.61% underwent surgery. After PSM, the surgery group showed significantly longer median overall survival (mOS) (26&#xa0;months vs. 11&#xa0;months) compared to the non-surgery group. Among the five machine learning models, logistic regression had the highest AUC of 0.760 on the validation set. Therefore, we used a logistic regression model to construct a nomogram. This tool identified beneficiary and non-beneficiary groups, with the former having a longer mOS (30&#xa0;months vs. 10&#xa0;months).</p> Conclusions <p>Overall, PTR in mNSCLC-NETs could prolong patients survival, and the web-based nomogram can predict patients who may benefit from surgery. This tool may aid clinicians in patient counseling and personalized decision-making.</p>

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Primary tumor resection: a new hope or an old illusion for patients with metastatic non-small cell lung neuroendocrine tumors?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Hongquan Xing,
  • Weichang Yang,
  • Shanshan Cai,
  • Linmin Xiong,
  • Guofeng Zhu,
  • Xinyi Zhang,
  • Xiaoqun Ye

摘要

Objectives

This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may benefit from surgery in terms of survival.

Methods

We extracted information on mNSCLC-NET patients from the SEER database. Propensity score matching was used to eliminate bias between surgery and non-surgery groups. The effect of PTR on prognosis was assessed via Kaplan‒Meier analysis with the log-rank test and the Cox proportional hazards model. Feature selection was performed via the Boruta algorithm. Model building utilized fivefold cross-validation and applied five machine learning algorithms. The optimal model was selected and used to construct a visual network nomogram.

Results

Among the 1,776 eligible patients, 12.61% underwent surgery. After PSM, the surgery group showed significantly longer median overall survival (mOS) (26 months vs. 11 months) compared to the non-surgery group. Among the five machine learning models, logistic regression had the highest AUC of 0.760 on the validation set. Therefore, we used a logistic regression model to construct a nomogram. This tool identified beneficiary and non-beneficiary groups, with the former having a longer mOS (30 months vs. 10 months).

Conclusions

Overall, PTR in mNSCLC-NETs could prolong patients survival, and the web-based nomogram can predict patients who may benefit from surgery. This tool may aid clinicians in patient counseling and personalized decision-making.