J Cancer 2021; 12(18):5583-5592. doi:10.7150/jca.56702 This issue Cite

Research Paper

Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data

Siwei Pan1,2, Wen An1,2, Yuen Tan1,2, Qingchuan Chen1,2, Pengfei Liu1,2, Huimian Xu1,2,✉

1. Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning, China
2. Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical University, Shenyang, China

Citation:
Pan S, An W, Tan Y, Chen Q, Liu P, Xu H. Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data. J Cancer 2021; 12(18):5583-5592. doi:10.7150/jca.56702. https://www.jcancer.org/v12p5583.htm
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Abstract

Graphic abstract

Background: Patients with early gastric cancer (EGC) must suffer reoperation if diagnosed with a high possibility of lymph node (LN) metastasis. The purpose of the current study was to develop and validate a model to predict the risk of LN metastasis in elderly patients before endoscopic resection.

Methods: A total of 1911 EGC patients who had undergone radical surgery were selected and assigned randomly (2:1) to either the training cohort or the validation cohort. A nomogram was established based on the univariate and multivariate logistic regression models using the training cohort. Cox proportional hazards regression models were applied to identify the prognostic factors in univariate and multivariable analyses.

Results: Three variables—tumor size, grade, and T stage—were derived from the multivariate analyses in the training cohort and incorporated into the nomogram. The AUC of the nomogram was 0.732 in the training cohort and 0.706 in the validation cohort. There were significant differences in survival among patients with different degrees of LN metastasis risk (training cohort: five-year disease-specific survival (DSS): low risk 88.1% vs. moderate risk 80.0% vs. high risk 72.9%, P < 0.001; validation cohort five-year DSS: low risk 89.0% vs. moderate risk 84.3% vs. high risk 72.2%, P < 0.001). The LN metastasis risk assessed from the model was also an independent prognostic factor.

Conclusion: We established a nomogram that accurately predicts LN metastasis risk for elderly patients with EGC before endoscopic resection to avoid further injury from reoperation.

Keywords: Early gastric cancer, lymph node metastasis, elderly patients, nomogram


Citation styles

APA
Pan, S., An, W., Tan, Y., Chen, Q., Liu, P., Xu, H. (2021). Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data. Journal of Cancer, 12(18), 5583-5592. https://doi.org/10.7150/jca.56702.

ACS
Pan, S.; An, W.; Tan, Y.; Chen, Q.; Liu, P.; Xu, H. Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data. J. Cancer 2021, 12 (18), 5583-5592. DOI: 10.7150/jca.56702.

NLM
Pan S, An W, Tan Y, Chen Q, Liu P, Xu H. Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data. J Cancer 2021; 12(18):5583-5592. doi:10.7150/jca.56702. https://www.jcancer.org/v12p5583.htm

CSE
Pan S, An W, Tan Y, Chen Q, Liu P, Xu H. 2021. Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data. J Cancer. 12(18):5583-5592.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
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