Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

Zhang, Yujie; Yu, Chaoran

doi:10.21037/jgo-20-536

Cited by 18 publications

(12 citation statements)

References 21 publications

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“…Secondly, we conducted the stepwise regression to eliminate variables with multicollinearity, while they did not conduct such analysis to eliminate the multicollinearity between variables. We believe our study design is more rational than others, and our predictive models are more convincing (39). The pathologic type might affect the prognosis of GC patients.…”

Section: Discussionmentioning

confidence: 81%

A predictive model for early death in elderly patients with gastric cancer: A population-based study

et al. 2022

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BackgroundThe mean age of gastric cancer (GC) patients has increased due to the aging society. Elderly GC patients with poor physical status tend to develop complications during the treatment courses, which cause early death. This study aimed to identify risk factors and establish nomograms for predicting total early death and cancer-specific early death in elderly GC patients.MethodsData for elderly GC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to a training cohort and a validation cohort. The univariate logistic regression model and backward stepwise logistic regression model were used to identify independent risk factors for early death. Nomograms were constructed to predict the overall risk of early death and their performance was validated by receiver operating characteristic (ROC) curve, calibration curve, decision curve analyses (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) in both training and validation cohorts.ResultsAmong the 3102 enrolled patients, 1114 patients died within three months from the first diagnosis and 956 of them died due to cancer-specific causes. Non-Asian or Pacific Islander (API) race, non-cardia/fundus or lesser/greater curvature, higher AJCC stage, no surgery and no chemotherapy were all related to a high risk of both all-cause early death and cancer-specific early death. Higher T stage and N0 stage were only positively related to total early mortality, while liver metastasis was only positively related to cancer-specific early mortality. Based on these identified factors, two nomograms were developed for predicting the risk of all-cause and cancer-specific early death, which showed good performance with the AUC of the nomograms were 0.775 and 0.766, respectively. The calibration curves, DCAs, NRI, and IDI also confirmed the value of these nomograms.ConclusionsThese nomogram models were considered a practical tool to identify the early death of elderly GC patients and help provide a more individualized treatment strategy.

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Section: Discussionmentioning

confidence: 81%

A predictive model for early death in elderly patients with gastric cancer: A population-based study

et al. 2022

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“…Still, they have failed to differentiate between local and radical resection and have not presented reliable postoperative prognostic models. Nomograms have been proved to have superior predictive accuracy than the current AJCC stage in previous analyses of prognostic cancer models after surgical resection, such as gastric cancer, lung cancer, cholangiocarcinoma, and other malignancies (35)(36)(37). To the best of our knowledge, this was the first study to establish and validate a prognostic nomogram that was not inferior to the AJCC staging system to predict 1-,3-and 5year OS for osteosarcoma patients after surgical resection.…”

Section: Discussionmentioning

confidence: 91%

“…In addition, as detailed in previous studies, the algorithms in X-tile enable quite reliable optimal cut-point analysis, determine the best cut-off value for continuous variables such as age and tumor size, or create survival-based risk stratification systems (24, 31). As a result, it has been frequently utilized in the survival analysis of malignancies such as breast cancer, cholangiocarcinoma, gastric carcinoma, osteosarcoma, and others (12,20,35,37,38,41). In this study, a risk stratification system was constructed based on the nomogram scores of all patients.…”

Section: Zhang Et Al and Zhao Et Al Established Nomograms For Specifi...mentioning

confidence: 99%

Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Gao

Wang

et al. 2022

Front. Public Health

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BackgroundPrevious prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram.MethodsWe enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultThis study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726–0.755) vs. (HR: 0.632, 95% CI: 0.619–0.645); Validation set (HR: 0.735, 95% CI: 0.718–0.753) vs. (HR: 0.635, 95% CI: 0.619–0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits.ConclusionThis study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making.

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“…Therefore, the establishment of an evaluation system with good diagnosis and prognosis screening is particularly important for the diagnosis and treatment of GC. At present, in addition to the TNM (tumor node metastasis) staging system, which is commonly used in clinical practices, many studies (Eom et al, 2015;Tonello et al, 2021;Zhang and Yu, 2021) have proposed the construction of new classification and prognosis-related prediction models. However, most of these models are based on the general data and clinical pathological data of patients, thereby having a certain lag in the evaluation of tumor prognosis as compared to molecular indicators, such as genes.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Liao

et al. 2022

Front. Genet.

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Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC.Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set.Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056–1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway.Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

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Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

Cited by 18 publications

References 21 publications

A predictive model for early death in elderly patients with gastric cancer: A population-based study

A predictive model for early death in elderly patients with gastric cancer: A population-based study

Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

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