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Objectives <p>The role of lymphadenectomy in endometrial cancer management remains controversial. This study aimed to identify clinicopathological characteristics and serum tumor markers that predict lymph node metastasis (LNM), and to develop a nomogram model integrating these parameters to predict individual LNM risk and guide personalized surgical planning.</p> Methods <p>Retrospective analysis was conducted on clinicopathological data and serum tumor marker levels for endometrial cancer patients who underwent surgical staging, including total hysterectomy, bilateral salpingo-oophorectomy, pelvic lymphadenectomy with or without para-aortic lymphadenectomy, at the Third Affiliated Hospital of Nanjing Medical University and Shanghai East Hospital of Tongji University from January 2008 to December 2024. Independent predictors of LNM were determined through univariate and multivariate logistic regression analyses and optimal subset selection. A nomogram model was developed using these variables. Predictive performance and accuracy were evaluated via receiver operating characteristic (ROC) curves and calibration plots.</p> Results <p>Among 506 eligible patients, 39 (7.7%) had LNM. Multivariate logistic regression and optimal subset analysis identified ovarian invasion (OR: 33.233, 95% CI 8.322–132.708), depth of myometrial invasion (OR: 27.223, 95% CI 10.989–67.436, <i>P &lt;</i> 0.001), LVSI (OR: 13.129, 95% CI 6.191–27.841, <i>P &lt;</i> 0.001), histological grade (OR: 2.933, 95% CI 1.800–4.778, <i>P &lt;</i> 0.001), and CA199 levels (OR: 1.005, 95% CI 1.002–1.009, <i>P &lt;</i> 0.004) as independent predictors of LNM. The nomogram demonstrated an AUC of 0.9106 (95% CI 0.8643–0.9569), specificity of 0.7923, sensitivity of 0.8974, accuracy of 0.8084, and negative predictive value of 0.9893. Calibration analysis showed strong agreement between predicted and observed outcomes. Decision curve analysis (DCA) indicated that the model offered clinical benefit.</p> Conclusion <p>The nomogram provides accurate LNM risk stratification in endometrial cancer, serving as a valuable tool to optimize lymphadenectomy strategies based on individual risk profiles.</p>

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A nomogram model based on dual-center data predicts lymph node metastasis in endometrial cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Liu Junling,
  • Chen Jiming,
  • Wang Wenjie,
  • Zheng Yafeng,
  • Li Fang

摘要

Objectives

The role of lymphadenectomy in endometrial cancer management remains controversial. This study aimed to identify clinicopathological characteristics and serum tumor markers that predict lymph node metastasis (LNM), and to develop a nomogram model integrating these parameters to predict individual LNM risk and guide personalized surgical planning.

Methods

Retrospective analysis was conducted on clinicopathological data and serum tumor marker levels for endometrial cancer patients who underwent surgical staging, including total hysterectomy, bilateral salpingo-oophorectomy, pelvic lymphadenectomy with or without para-aortic lymphadenectomy, at the Third Affiliated Hospital of Nanjing Medical University and Shanghai East Hospital of Tongji University from January 2008 to December 2024. Independent predictors of LNM were determined through univariate and multivariate logistic regression analyses and optimal subset selection. A nomogram model was developed using these variables. Predictive performance and accuracy were evaluated via receiver operating characteristic (ROC) curves and calibration plots.

Results

Among 506 eligible patients, 39 (7.7%) had LNM. Multivariate logistic regression and optimal subset analysis identified ovarian invasion (OR: 33.233, 95% CI 8.322–132.708), depth of myometrial invasion (OR: 27.223, 95% CI 10.989–67.436, P < 0.001), LVSI (OR: 13.129, 95% CI 6.191–27.841, P < 0.001), histological grade (OR: 2.933, 95% CI 1.800–4.778, P < 0.001), and CA199 levels (OR: 1.005, 95% CI 1.002–1.009, P < 0.004) as independent predictors of LNM. The nomogram demonstrated an AUC of 0.9106 (95% CI 0.8643–0.9569), specificity of 0.7923, sensitivity of 0.8974, accuracy of 0.8084, and negative predictive value of 0.9893. Calibration analysis showed strong agreement between predicted and observed outcomes. Decision curve analysis (DCA) indicated that the model offered clinical benefit.

Conclusion

The nomogram provides accurate LNM risk stratification in endometrial cancer, serving as a valuable tool to optimize lymphadenectomy strategies based on individual risk profiles.