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Updated: Jul 16, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk factors and nomogram development for lymph node metastasis in early-onset early-stage gastric cancer: a
Binghe Zhao1,2, Mingyu Gu1, Zijian Wang1
1Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
Background:
The incidence of early onset gastric cancer(EOGC) is increasing. However, few studies have focused on early onset early stage gastric cancer(EEGC). The aim of this study was to determine the threshold age of patients with EOGC, identify the clinicopathological characteristics associated with lymph node metastasis(LNM) in EEGC, and develop a predictive model for LNM in EEGC.
Methods:
A retrospective cohort study was conducted, including 1765 patients with early-stage gastric cancer. Logistic inflection point and stratified analysis were used to determine the threshold age. 266 patients met the criteria for EEGC and were included for further analysis. The patients were divided into two groups for the purposes of the study: a training dataset and an external validation dataset. The division of patients into these two groups was conducted in accordance with the time of surgery, with the ratio of patients in each group being approximately 7:3.Univariate and multivariate logistic regression analysis were used to identify LNM risk factors. A predictive nomogram was developed and validated using calibration plots and the area under the curve (AUC).The constructed logistic regression model was then validated using the external validation dataset.
Results:
The threshold age for EOGC was determined to be 45 years. Of the 266 patients with EEGC, 20.7% had LNM. Tumor maximum diameter and lymphovascular invasion were identified as independent risk factors for LNM. The nomogram demonstrated high predictive accuracy, with an AUC of 0.809.
Conclusions:
This study demonstrated that tumor maximum diameter and lymphovascular invasion were independent risk factors for LNM in EEGC. The predictive nomogram showed promising accuracy and might assist in identifying patients at higher risk of LNM, potentially informing treatment strategies. Given the relatively high LNM rate, endoscopic submucosal dissection may not be suitable for EEGC patients. Further large-scale multicenter studies are needed to deepen the understanding of this population and to confirm these findings.
