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

Wei-Hao Zhang1, Meng-Di Huang2, Yan-Ling Tu3

  • 1Department of General Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, No. 55, Zhenhai Road, Siming District, Xiamen, 361003, Fujian, China.

Clinical & Translational Oncology : Official Publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
|April 12, 2025
PubMed
Summary

A new clinical model accurately predicts lymph node metastasis (LNM) risk in young colon cancer (CC) patients. This tool helps identify high-risk individuals for personalized treatment strategies.

Keywords:
Colon cancerLymph node metastasisPrediction modelYoung patient

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Area of Science:

  • Oncology
  • Clinical Prediction Modeling

Background:

  • Young colon cancer (CC) patients present unique challenges.
  • Predicting lymph node metastasis (LNM) risk in this demographic is crucial for effective management.

Purpose of the Study:

  • To develop and validate a clinical model for predicting LNM risk in patients under 40 with CC.
  • To address the unmet clinical need for individualized risk assessment in young CC patients.

Main Methods:

  • Utilized data from 2,360 CC patients under 40 from the SEER database.
  • Developed a logistic regression model to identify risk factors (T stage, tumor site, grade, histology).
  • Constructed a weighted scoring system and evaluated model performance using C-statistics and H-L tests.

Main Results:

  • Identified T stage, tumor site, grade, and histology as key risk factors for LNM.
  • Achieved an AUC-ROC of 0.66 in both development and validation cohorts, indicating acceptable discrimination.
  • Demonstrated good model calibration in both cohorts via H-L tests and calibration plots.

Conclusions:

  • The developed clinical model accurately identifies young CC patients at high risk of LNM.
  • This model provides a valuable individualized clinical reference for risk stratification and treatment planning.