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Background <p>The treatment of early gastric cancer (EGC) is contingent upon the status of lymph node metastasis (LNM). Accurate preoperative prediction of LNM is critical for reducing unnecessary surgeries. This study seeks to evaluate the risk factors for LNM in submucosal EGC and develop a predictive model to optimize therapeutic decision-making.</p> Methods <p>A retrospective analysis was performed on clinical data from 389 patients with T1b-stage EGC who underwent radical gastrectomy. Univariate and multivariate analyses were conducted to identify independent risk factors, followed by the development of a nomogram to predict LNM. The model’s efficacy was validated through receiver operating characteristic curves, calibration curves, and decision curve analysis.</p> Results <p>Of the 389 patients, 77 had LNM. Logistic regression analysis identified gender, CA199 levels, tumor location, degree of differentiation, presence of ulcers, and lymph node enlargement on CT as independent risk factors for LNM. A nomogram was constructed to assess the risk of LNM, demonstrating strong predictive accuracy with an area under the curve of 0.82 in the training set and 0.74 in the validation set, along with good sensitivity and positive predictive value.</p> Conclusions <p>This study presents a reliable preoperative nomogram to estimate the likelihood of LNM in submucosal EGC, providing valuable guidance for determining the most effective treatment strategies for patients.</p>

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Nomogram for pre-procedural prediction of lymph node metastasis in patients with submucosal early gastric cancer

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • Wenhao Yu,
  • Zijie Xu,
  • Ben Li,
  • Mengli Zi,
  • Jun Ren,
  • Wei Wang,
  • Qiannan Sun,
  • Qi Zhang,
  • Daorong Wang

摘要

Background

The treatment of early gastric cancer (EGC) is contingent upon the status of lymph node metastasis (LNM). Accurate preoperative prediction of LNM is critical for reducing unnecessary surgeries. This study seeks to evaluate the risk factors for LNM in submucosal EGC and develop a predictive model to optimize therapeutic decision-making.

Methods

A retrospective analysis was performed on clinical data from 389 patients with T1b-stage EGC who underwent radical gastrectomy. Univariate and multivariate analyses were conducted to identify independent risk factors, followed by the development of a nomogram to predict LNM. The model’s efficacy was validated through receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

Of the 389 patients, 77 had LNM. Logistic regression analysis identified gender, CA199 levels, tumor location, degree of differentiation, presence of ulcers, and lymph node enlargement on CT as independent risk factors for LNM. A nomogram was constructed to assess the risk of LNM, demonstrating strong predictive accuracy with an area under the curve of 0.82 in the training set and 0.74 in the validation set, along with good sensitivity and positive predictive value.

Conclusions

This study presents a reliable preoperative nomogram to estimate the likelihood of LNM in submucosal EGC, providing valuable guidance for determining the most effective treatment strategies for patients.