Prognostic Nomograms for Nonelderly Adults with Gastric Signet Ring Cell Carcinoma

Wang, Hui; Yao, Peng; Huang, Qi; Wu, Jingjing; Zhang, Mingjun

doi:10.1155/2021/1274527

Cited by 5 publications

(6 citation statements)

References 36 publications

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“…By browsing titles and abstracts, 656 articles were discarded. The rest of 68 articles were read in full text, among which 48 articles reported irrelevant data, 8 articles did not have enough data to extract the results we wanted, and finally, 12 articles were included [18,21,[27][28][29][30][31][32][33][34][35][36]. Figure 1 shows the literature screening process.…”

Section: Literature Retrieval and Studymentioning

confidence: 99%

Clinicopathological Features and Prognostic-Related Risk Factors of Gastric Signet Ring Cell Carcinoma: A Meta-Analysis

Guo

Wang

Tian

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Gastric signet ring cell carcinoma (SRCC) has shown a growth growing trend worldwide, but its clinicopathological features and prognostic-related risk factors have not been systematically studied. This systematic review was devoted to this. Method. PubMed, Embase, Cochrane Library, and Web of Science databases were retrieved, and retrospective cohort studies comparing clinicopathological features and related risk factors in SRCC patients were included. Results. In SRCC patient population, males were more than females (male, OR = 1.38 , 95% CI: 1.20-1.60); N3 patients were more than N0-2 patients (N0-2, OR = 3.19 , 95% CI: 1.98-5.15); M1 patients were more than M0 patients (M0, OR = 3.30 , 95% CI: 1.88-5.80); patients with tumor > 5 cm were more than those with tumor ( ≤ 5 cm, OR = 7.36 , 95% CI: 1.33-40.60). Patients with age < 60 years ( age ≥ 60 years, OR = 1.03 , 95% CI: 1.01-1.05), lymphatic vessel invasion (no, OR = 1.74 , 95% CI: 1.03-2.45), T2 (T1, OR = 1.17 , 95% CI: 1.07-1.28) and T4 (T1, OR = 2.55 , 95% CI: 2.30-2.81) stages, and N1 (N0, OR = 1.73 , 95% CI: 1.08-2.38), N2 (N0, OR = 2.24 , 95% CI: 1.12-3.36), and N3 (N0, OR = 3.45 , 95% CI: 1.58-5.32) stages had higher hazard ratio (HR). Conclusion. SRCC may occur frequently in male. Age, lymphatic vessel invasion, TN, and M stage may be risk factors for poor prognoses of SRCC patients.

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Section: Literature Retrieval and Studymentioning

confidence: 99%

Clinicopathological Features and Prognostic-Related Risk Factors of Gastric Signet Ring Cell Carcinoma: A Meta-Analysis

Guo

Wang

Tian

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The studies carried out by Wang et al, Zhang et al, and Guo et al also had similar problems [ 13 , 14 , 28 ]. Wei et al collected 1,030 patients from the SEER database and constructed CSS prognostic model.…”

Section: Discussionmentioning

confidence: 87%

“…Accurate prediction is essential for the survival of patients with nonmetastatic GSRC and medical decisionmaking. Previous regression analysis-based models have been developed to predict the survival of patients with non-metastatic GSRC [13][14][15]. Nonetheless, these models are commonly based on the assumption that each parameter, and the survival outcomes of GSRC were linear dependent (regression model).…”

Section: Introductionmentioning

confidence: 99%

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Chen

Lin

et al. 2022

Computational and Mathematical Methods in Medicine

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Background and Aims. Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach. Methods. We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC). Results. In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity. Conclusion. The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.

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“…Unfortunately, the SEER dataset missed that information. Based on these conclusions, we focused on the population with tumor cells with a G3-4 differentiation degree, roughly discussed the impacts of the TNM staging system on the outcomes of GSRCC patients and ignored the tumor size and detailed location in the stomach compared to prior SEER studies [8,13,14,16].…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

Shao,

Li,

Lin

et al. 2024

Dig Dis

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Background: The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC). Patients and Methods: A total of 3408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot and area under the receiver operating characteristic (ROC) curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort. Results: We identified age, tumor lymph node metastasis (TNM) staging system, surgery and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751–0.774) and 0.766 (95% CI: 0.748–0.784) and a C-index of CSS of 0.765 (95% CI: 0.753–0.777) and 0.773 (95% CI: 0.755–0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets. Conclusion: The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.

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Prognostic Nomograms for Nonelderly Adults with Gastric Signet Ring Cell Carcinoma

Cited by 5 publications

References 36 publications

Clinicopathological Features and Prognostic-Related Risk Factors of Gastric Signet Ring Cell Carcinoma: A Meta-Analysis

Clinicopathological Features and Prognostic-Related Risk Factors of Gastric Signet Ring Cell Carcinoma: A Meta-Analysis

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

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