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Background <p>Preoperative lymph node metastasis (LNM) risk stratification is crucial for selecting endoscopic resection or colectomy in T1 right-sided colon cancer (T1 RCC), yet validated tools integrating preoperative predictive indicators remain lacking.</p> Methods <p>This retrospective study included 628 patients with T1 RCC. Single-predictor and multi-predictor logistic regression models identified independent preoperative predictors of LNM to construct a nomogram. Model performance was assessed using area under the curve (AUC) and calibration metrics.</p> Results <p>Six preoperative factors constituted independent predictors of LNM: age (OR = 0.92, 95% CI: 0.88–0.97), tumor location (hepatic flexure: OR = 3.50, 95% CI: 1.68–7.31), poorly differentiated tumor (OR = 4.21, 95% CI: 2.23–7.94), CT-detected lymphadenopathy (OR = 5.14, 95% CI: 2.79–9.46), elevated CEA (OR = 3.26, 95% CI: 1.77–6.01), and elevated CA 19-9 (OR = 1.98, 95% CI: 1.04–3.77). This predictive model demonstrated high discriminatory ability: 0.84 AUC in training preserved at 0.83 upon validation. Calibration curves demonstrated optimal agreement with observed outcomes, supported by a non-significant Hosmer–Lemeshow test. Decision curve analysis further established robust clinical net benefit across threshold probabilities.</p> Conclusion <p>This preoperative predictive model accurately quantifies the risk of LNM in T1 RCC using routine parameters, guiding individualized treatment decisions and preventing unnecessary colectomy in low-risk patients.</p>

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Nomogram for preoperative prediction of lymph node metastasis in patients with early submucosal right-sided colon cancer: a dual-center retrospective cohort study

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Fei Han,
  • Yuling Liu,
  • Yaning Wang,
  • Jie Wang,
  • Weiming Xiao,
  • Xue Dong,
  • Xiaolei Shi,
  • Yanbing Ding

摘要

Background

Preoperative lymph node metastasis (LNM) risk stratification is crucial for selecting endoscopic resection or colectomy in T1 right-sided colon cancer (T1 RCC), yet validated tools integrating preoperative predictive indicators remain lacking.

Methods

This retrospective study included 628 patients with T1 RCC. Single-predictor and multi-predictor logistic regression models identified independent preoperative predictors of LNM to construct a nomogram. Model performance was assessed using area under the curve (AUC) and calibration metrics.

Results

Six preoperative factors constituted independent predictors of LNM: age (OR = 0.92, 95% CI: 0.88–0.97), tumor location (hepatic flexure: OR = 3.50, 95% CI: 1.68–7.31), poorly differentiated tumor (OR = 4.21, 95% CI: 2.23–7.94), CT-detected lymphadenopathy (OR = 5.14, 95% CI: 2.79–9.46), elevated CEA (OR = 3.26, 95% CI: 1.77–6.01), and elevated CA 19-9 (OR = 1.98, 95% CI: 1.04–3.77). This predictive model demonstrated high discriminatory ability: 0.84 AUC in training preserved at 0.83 upon validation. Calibration curves demonstrated optimal agreement with observed outcomes, supported by a non-significant Hosmer–Lemeshow test. Decision curve analysis further established robust clinical net benefit across threshold probabilities.

Conclusion

This preoperative predictive model accurately quantifies the risk of LNM in T1 RCC using routine parameters, guiding individualized treatment decisions and preventing unnecessary colectomy in low-risk patients.