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Aims <p>Developing a clinical model to predict the individual risk of lymph node metastasis (LNM) in young colon cancer (CC) patients may address an unmet clinical need.</p> Methods <p>A total of 2,360 CC patients under 40 years old were extracted from the SEER database and randomly divided into development and validation cohorts. Risk factors for LNM were identified by using a logistic regression model. A weighted scoring system was built according to beta coefficients (β) calculated by a logistic regression model. Model discrimination was evaluated by C-statistics, model calibration was evaluated by H-L test and calibration plot.</p> Results <p>Risk factors were identified as T stage, tumor site, grade and histology. The area under the receiver operating characteristic curve (AUC-ROC) was 0.66 in both cohorts, indicating acceptable discriminatory power. The H-L test showed good calibration in the development cohort (χ<sup>2</sup>=2.869, P=0.837) and validation cohort (χ<sup>2</sup>=10.103, P=0.120) which also had been proved by calibration plot. Patients with total risk score of 0-1, 2-3 and 4-6 were considered as low, medium and high risk group.</p> Conclusion <p>This clinical risk prediction model is accurate enough to identify young CC patients with high risk of LNM and can further provide individualized clinical reference.</p>

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Prediction of lymph node metastasis in stage I–III colon cancer patients younger than 40 years

  • 期刊论文

  • 发表日期：

    2025年4月12日

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  • Wei-Hao Zhang,
  • Meng-Di Huang,
  • Yan-Ling Tu,
  • Kun-Zhai Huang,
  • Chao-Jun Wang,
  • Zhao-Hui Liu,
  • Rui-Sheng Ke

摘要

Aims

Developing a clinical model to predict the individual risk of lymph node metastasis (LNM) in young colon cancer (CC) patients may address an unmet clinical need.

Methods

A total of 2,360 CC patients under 40 years old were extracted from the SEER database and randomly divided into development and validation cohorts. Risk factors for LNM were identified by using a logistic regression model. A weighted scoring system was built according to beta coefficients (β) calculated by a logistic regression model. Model discrimination was evaluated by C-statistics, model calibration was evaluated by H-L test and calibration plot.

Results

Risk factors were identified as T stage, tumor site, grade and histology. The area under the receiver operating characteristic curve (AUC-ROC) was 0.66 in both cohorts, indicating acceptable discriminatory power. The H-L test showed good calibration in the development cohort (χ2=2.869, P=0.837) and validation cohort (χ2=10.103, P=0.120) which also had been proved by calibration plot. Patients with total risk score of 0-1, 2-3 and 4-6 were considered as low, medium and high risk group.

Conclusion

This clinical risk prediction model is accurate enough to identify young CC patients with high risk of LNM and can further provide individualized clinical reference.