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Updated: Jul 7, 2025
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External
Wenxiu An1,2, Lijie Bao1,2, Chenyu Wang1,2
1Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang City, Liaoning Province, People's Republic of China.
Background:
Gastric cancer (GC) has a poor prognosis, particularly in patients with liver metastasis (LM). This study aims to identify relevant factors associated with the occurrence of LM in GC patients and factors influencing the prognosis of gastric cancer with liver metastasis (GCLM) patients, in addition to developing diagnostic and prognostic nomograms specifically.
Patients And Methods:
Overall, 6184 training data were from the Surveillance, Epidemiology, and End Results (SEER) database from 2011 to 2015. 1527 validation data were from our hospital between January 2018 and December 2022. Logistic regression was used to identify the risk factors associated with the occurrence of LM in GC patients, Cox regression was used to confirm the prognostic factors of GCLM patients. Two nomogram models were established to predict the risk and overall survival (OS) of patients with GCLM. The performance of the two models was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves.
Results:
A nomogram included five independent factors from multivariate logistic regression: sex, lymph node removal, chemotherapy, T stage and N stage were constructed to calculate the possibility of LM. Internal and external verifications of AUC were 0.786 and 0.885, respectively. The other nomogram included four independent factors from multivariate Cox regression: surgery at primary site, surgery at other site, chemotherapy, and N stage were constructed to predict OS. C-index for internal and external validations were 0.714 and 0.702, respectively, and the calibration curves demonstrated the robust discriminative ability of the models.
Conclusion:
Based on the SEER database and validation data, we defined effective nomogram models to predict risk and OS in patients with GCLM. They have important value in clinical decision-making and personalized treatment.
