ArticleBMC cancer2025
Development of a nomogram based on body composition and tumor burden indicators to predict lymph node metastasis in patients with T1 esophageal squamous cell carcinoma.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Accuracy of machine learning in detecting lymph node metastasis of esophageal cancer: a systematic review and meta-analysis.Translational cancer research · 2026Article
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7 authors.
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Abstract
backgroundTreatment strategies for patients with early esophageal cancer are diverse, and lymph node metastasis (LNM) remains a critical determinant in therapeutic decision-making and prognosis. However, current methods for predicting LNM in early esophageal cancer are limited.
aimTo evaluate the impact of body composition parameters on LNM in early esophageal squamous cell carcinoma (ESCC) and to develop a nomogram based on body composition and tumor burden indicators for assessing LNM in T1 ESCC.
methodsA retrospective analysis was conducted on clinical data and CT imaging of 243 patients with histologically confirmed T1 ESCC who underwent radical surgery. Cross-sectional areas of skeletal muscle, subcutaneous fat, and visceral fat at the level of the L3 vertebra were quantified using ImageJ software. Univariate and multivariate analyses were conducted to identify risk factors associated with LNM. A predictive nomogram was then constructed based on the multivariate results and evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
resultsAmong the 243 patients, 38 cases exhibited LNM, yielding an incidence rate of 15.64%. Multivariate analysis identified tumor size, lymphovascular invasion (LVI), visceral fat area (VFA), and T1 substage as independent risk factors. The AUC values for size, LVI, VFA, and T1 substage were 0.628, 0.651, 0.767, and 0.619, respectively. The nomogram constructed based on these four variables demonstrated strong predictive performance, with AUCs of 0.8331 in the training set and 0.8343 in the validation set, both surpassing the predictive capacity of individual indicators. These findings underscore the nomogram's superior prognostic accuracy compared to any single pathological parameter. The calibration curve and DCA demonstrated its considerable potential for clinical application.
conclusionThis nomogram, integrating body composition and tumor burden indicators, provides an individualized and practical tool for predicting LNM in early ESCC, supporting more informed clinical decision-making.
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