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Updated: Jul 17, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting lymph node metastasis in early gastric cancer
Jing-Yang He1, Meng-Xuan Cao1, En-Ze Li1
1Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, China.
Background:
Lymph node metastasis (LNM) significantly impacts the treatment and prognosis of early gastric cancer (EGC). Consequently, the precise prediction of LNM risk in EGC patients is essential to guide the selection of appropriate surgical approaches in clinical settings.
Aim:
To develop a novel nomogram risk model for predicting LNM in EGC patients, utilizing preoperative clinicopathological data.
Methods:
Univariate and multivariate logistic regression analyses were performed to examine the correlation between clinicopathological factors and LNM in EGC patients. Additionally, univariate Kaplan-Meier and multivariate Cox regression analyses were used to assess the influence of clinical factors on EGC prognosis. A predictive model in the form of a nomogram was developed, and its discrimination ability and calibration were also assessed.
Results:
The incidence of LNM in the study cohort was 19.6%. Multivariate logistic regression identified tumor size, location, degree of differentiation, and pathological type as independent risk factors for LNM in EGC patients. Both tumor pathological type and LNM independently affected the prognosis of EGC. The model's performance was reflected by an area under the curve of 0.750 [95% confidence interval (CI): 0.701-0.789] for the training group and 0.763 (95%CI: 0.687-0.838) for the validation group.
Conclusion:
A clinical prediction model was constructed (using tumor size, low differentiation, location in the middle-lower region, and signet ring cell carcinoma), with its score being a significant prognosis indicator.
