Development and Validation of Prognostic Nomogram Based on Negative Lymph Node Count for Patients with Gastric Signet Ring Cell Carcinoma
10.3971/j.issn.1000-8578.2022.22.0014
- VernacularTitle:基于阴性淋巴结数目的胃印戒细胞癌预后评估模型的建立与验证
- Author:
Jinzhou LI
1
;
Wenjie WANG
;
Yalong YAO
;
Yanxi MU
;
Kang CHEN
;
Yimin SHEN
;
Zhou WANG
;
Zeping HUANG
;
Xiao CHEN
Author Information
1. The Second Clinical Medical College of Lanzhou University, Lanzhou 730000, China
- Publication Type:Research Article
- Keywords:
Gastric signet ring cell carcinoma;
Negative lymph node count;
Prognosis;
Nomogram
- From:
Cancer Research on Prevention and Treatment
2022;49(9):923-930
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the influence of negative lymph node count (NLNC) on the prognosis of patients with gastric signet ring cell carcinoma (GSRC) and develop a prognostic nomogram based on NLNC. Methods On the basis of the SEER database, 2 101 patients diagnosed with GSRC were collected and randomly divided into the modeling group and validation group to test the relationship between clinicopathological characteristics and the prognosis of GSRC. The multivariate Cox proportional hazard regression model was used to analyze the independent risk factors affecting overall survival and establish a prognostic prediction model. The consistency index (C-index), calibration curve, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to evaluate the accuracy and clinical applicability of the nomogram. Results All patients were divided according to the ratio of 7:3, with 1 473 in the modeling group and 628 in the validation group. NLNC > 10 (HR=0.578, 95%CI: 0.504-0.662, P < 0.001) was a protective factor for the prognosis of patients with GSRC, and the nomogram model was established based on multivariate Cox proportional hazards model. The C-index values of the nomogram were 0.737 (95%CI: 0.720-0.753) and 0.724 (95%CI: 0.699-0.749) in the modeling and validation groups, respectively, showing good discrimination. The calibration curves showed high consistency of the model. NRI=17.77%, continuous NRI=36.34%, and IDI=4.2% indicated that the model had positive returns compared with the traditional model. The DCA was far from the baseline, indicating that the model had good clinical applicability. Conclusion The increase in NLNC is a favorable factor for the prognosis of patients with GSRC, and a relatively accurate nomogram was established to predict the prognosis of patients with GSRC and help clinicians conduct individualized prognostic evaluations.
