Prognostic factors for liver metastasis in patients with small intestinal stromal tumor: A retrospective analysis of surveillance, epidemiology, and end results

Background Liver metastasis (LIM) is the most common distant site of metastasis in small intestinal stromal tumors (SISTs). The aim of this study was to determine the risk and prognostic factors associated with LIM in patients with SISTs. Methods Patients diagnosed with gastrointestinal stromal tumo...

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Bibliographic Details
Published in:World journal of surgery 2024-03, Vol.48 (3), p.598-609
Main Authors: Liu, Luojie, Zhang, Rufa, Qiao, Zhenguo, Ye, Ye, Xia, Kaijian, Feng, Yunfu, Xu, Xiaodan
Format: Article
Language:English
Subjects:
Databases, Factual
Humans
Intestinal Neoplasms - surgery
liver metastasis
Liver Neoplasms - surgery
Nomograms
Prognosis
prognosis factor
Retrospective Studies
risk factor
SEER
SEER Program
small intestinal stromal tumor
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Summary:Background Liver metastasis (LIM) is the most common distant site of metastasis in small intestinal stromal tumors (SISTs). The aim of this study was to determine the risk and prognostic factors associated with LIM in patients with SISTs. Methods Patients diagnosed with gastrointestinal stromal tumors between 2010 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models, as well as a Cox regression model were used to explore the risk factors associated with the development and prognosis of LIM. Additionally, the overall survival (OS) of patients with LIM was analyzed using the Kaplan‐Meier method. Furthermore, a predictive nomogram was constructed, and the model's performance was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results A total of 1582 eligible patients with SISTs were included, among whom 146 (9.2%) were diagnosed with LIM. Poor tumor grade, absence of surgery, later T‐stage, and no chemotherapy were associated with an increased risk of developing LIM. The nomogram prediction model achieved an AUC of 0.810, 95% Confidence Interval (CI) 0.773–0.846, indicating good performance, and the calibration curve showed excellent accuracy in predicting LIM. The OS rate of patients with LIM was significantly lower than that of patients without LIM (p 
ISSN:0364-2313
1432-2323
DOI:10.1002/wjs.12073