Surgical endoscopy · 2026
Nomogram for preoperative prediction of lymph node metastasis in patients with early submucosal right-sided colon cancer: a dual-center retrospective cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Han Fei, Liu Yuling, Wang Yaning, Wang Jie, Xiao Weiming, Dong Xue, Shi Xiaolei, Ding Yanbing
Аннотация
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.
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.