Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External Validation Based Study

An, Wenxiu; Bao, Lijie; Wang, Chenyu; Zheng, Mingxin; Zhao, Yan

doi:10.2147/ijgm.s434952

Cited by 2 publications

References 28 publications

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Distinct characteristics of distant metastasis in early-onset gastric cancer patients compared to late-onset patients: An observational study

Wu,

Shuai

2024

Medicine

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Presently, there is limited understanding of the features of distant metastasis in early-onset gastric cancer (GC). To explore these disparities, a retrospective study utilizing the Surveillance, Epidemiology, and End Results (SEER) database was undertaken. The SEER database was utilized to extract patient data, and multivariate logistic regression analysis was employed to identify the risk factors associated with distant metastasis and liver metastasis. Propensity score matching (PSM) was used to compare the occurrence of liver metastasis among patients based on their age at diagnosis. The study included 2684 early-onset GC patients and 33,289 late-onset GC patients. Preliminary data analysis indicated that early-onset GC patients exhibited more aggressive characteristics such as poor cell differentiation, advanced T stage, and a higher incidence of distant metastasis, excluding liver metastasis. Multivariate logistic regression analysis identified younger age as an independent risk factor for distant metastasis, along with T stage, lymph node metastasis (LNM), and tumor size (>3 cm). Another regression analysis revealed that younger age, diffuse type, and female gender were protective factors against liver metastasis. Through PSM, 3276 early-onset GC patients were matched with an equal number of late-onset GC patients, revealing that patients with early-onset GC had fewer instances of liver metastasis but a higher prevalence of distant metastasis. Our findings suggest that early-onset serves as a protective factor against liver metastasis in GC, while it poses a risk for distant metastasis, likely influenced by the increased prevalence of diffuse-type GC in early-onset patients.

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Distinct characteristics of distant metastasis in early-onset gastric cancer patients compared to late-onset patients: An observational study

Wu,

Shuai

2024

Medicine

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Development and Validation of a Cancer-Specific Early Death Prediction Model for Patients with Gastric Cancer with Liver Metastasis: Based on Machine Learning

Zhu,

Chen,

et al. 2024

Preprint

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Background Gastric cancer with liver metastasis (GCLM) patients typically have a grim prognosis and are at high risk of early mortality. This study aimed to predict cancer-specific early mortality and risk factors for GCLM patients through machine learning (ML) methods. Methods The data of patients with GCLM were obtained from the SEER database. LASSO regression, univariate and multivariate logistic regression analyses were employed to identify significant independent risk factors for cancer-specific early death (CSED). Models such as logistic regression (LR), decision tree (DT), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) were used to predict the CSED and extract important features. Tenfold cross-validation, receiver operating characteristic (ROC) curve analysis, accuracy, balance accuracy, precision, sensitivity, specificity, F1-score, precision‒recall (PR) curve analysis, calibration curve analysis and decision curve analysis (DCA) were utilized to assess the performance of the models. The DALEX package was used to compute feature importance. Results The study recruited a total of 3661 patients. A total of 1648 (45%) patients experienced CSED. Among the 7 ML models, the XGBoost model achieved the best performance. The top 6 most influential factors were chemotherapy, months from diagnosis to therapy, age, grade, N stage, and surgery in the XGBoost model, with chemotherapy being the most significant. Conclusion The XGBoost model might be applied to predict the CSED of GCLM patients, and chemotherapy was the most important feature in the XGBoost model. These results could offer crucial reference data to assist clinicians in making informed decisions beforehand.

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Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External Validation Based Study

Cited by 2 publications

References 28 publications

Distinct characteristics of distant metastasis in early-onset gastric cancer patients compared to late-onset patients: An observational study

Distinct characteristics of distant metastasis in early-onset gastric cancer patients compared to late-onset patients: An observational study

Development and Validation of a Cancer-Specific Early Death Prediction Model for Patients with Gastric Cancer with Liver Metastasis: Based on Machine Learning

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