Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

Nie, Duorui; Zheng, Hao; An, Guilin; Li, Jing

doi:10.1007/s00432-023-04796-x

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…In the training and testing set, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage Table S4, Supplemental Digital Content 4, http://links.lww.com/JS9/B744 , with P values less than 0.001 among all groups. The results showed that our nomogram had robust and more accurate prediction ability than the traditional AJCC staging system, which is consistent with previous published literature 25 – 27 .…”

Section: Resultssupporting

confidence: 91%

“…Krieg et al 24 constructed a nomogram to estimate the prognosis of GEP-NETs patients after surgery and Wu et al . 25 constructed a predictive model of GEP-NETs grading using preoperative laboratory and imaging parameters, while the relatively small sample size is the limitation for both works. Until now, no studies have combined substantial treatment factors with clinical factors to predict the prognosis of patients with GEP-NETs based on the nomogram.…”

Section: Introductionmentioning

confidence: 99%

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A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study

Wu,

Shang,

Zhang

et al. 2024

International Journal of Surgery

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Background: Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients. Methods: GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system. Results: From 2004 to 2015, a total of 42,662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients (n=7,564) were randomly divided into training (3,782 patients) and testing sets (3,782 patients). In the univariate analysis, sex, age, race, tumor location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumor size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumor location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804–0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812–0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range. Conclusion: A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. We highly recommend to use our nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study

Wu,

Shang,

Zhang

et al. 2024

International Journal of Surgery

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“…Wang utilized a column-line graph approach to construct a predictive model for postoperative survival in patients with gastric cardia adenocarcinoma, achieving a maximum c-index of 0.746 ( 40 ). Similar results were obtained in the study by Nie et al ( 41 ). And in this study, the best C-index of DeepSurv model reached 0.821.…”

Section: Discussionsupporting

confidence: 92%

Deep learning model for predicting postoperative survival of patients with gastric cancer

Zeng,

Song,

et al. 2024

Front. Oncol.

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BackgroundPrognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients.MethodsEleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI).ResultsIt was discovered that the DeepSurv model predicted postoperative gastric cancer patients’ overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model’s forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer.ConclusionIn this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.

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“…For instance, the GSRCC prediction model that was presented by Liu et al ( 5) and Zhang et al (7) did not predict CSS; instead, it only addressed the OS. Nie et al (8) only used data from the SEER database; hence, it is challenging to evaluate its accuracy and viability because there are insufficient external validations. Furthermore, a number of important clinicopathological parameters that have a substantial impact on patient survival-such as age, sex, and treatment type-are not taken into consideration by the GSRCC monitoring prediction models that are now in use.…”

Section: Discussionmentioning

confidence: 99%

“…Nomograms, which are commonly used for assessing cancer patient prognosis and personally predicting survival rates, are more suitable for clinical patient management than the TNM staging system. However, despite the establishment of several postoperative nomograms that have significantly contributed to the management of patients with GSRCC, some issues have arisen (5)(6)(7)(8). First, these models focused solely on the overall postoperative survival and did not predict cancer-specific survival.…”

Section: Introductionmentioning

confidence: 99%

A novel web-based dynamic prognostic nomogram for gastric signet ring cell carcinoma: a multicenter population-based study

Jiang,

Hu,

Shao

et al. 2024

Front. Immunol.

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BackgroundGastric signet ring cell carcinoma (GSRCC) is a rare and highly malignant disease with a poor prognosis. To assess the overall survival (OS) and cancer-specific survival (CSS) of patients with GSRCC, prognostic nomograms were developed and validated using common clinical factors.MethodsThis retrospective cohort study included patients diagnosed with GSRCC between 2011 and 2018 from the National Cancer Center (n = 1453) and SEER databases (n = 2745). Prognostic nomograms were established by identifying independent prognostic factors using univariate and multivariate Cox regression analyses. The calibration curve and C-index were used to assess the predictions. The clinical usefulness of the survival prediction model was further evaluated using the DCA and ROC curves. The models were internally validated in the training cohort and externally validated in the validation cohort. Two web servers were created to make the nomogram easier to use.ResultsPatients with GSRCC were divided into training (n = 2938) and validation (n = 1260) cohorts. The nomograms incorporated six predictors: age, race, tumor site, tumor size, N stage, T stage, and AJCC stage. Excellent agreement was observed between the internal and exterior calibration plots for the GSRCC survival estimates. The C-index and area under the ROC curve were roughly greater than 0.7. Both nomograms had adequate clinical efficacy, as demonstrated by the DCA plots. Furthermore, we developed a dynamic web application utilizing the constructed nomograms available at https://jiangyujuan.shinyapps.io/OS-nomogram/ and https://jiangyujuan.shinyapps.io/DynNomapp-DFS/.ConclusionWe developed web-based dynamic nomograms utilizing six independent prognostic variables that assist physicians in estimating the OS and CSS of patients with GSRCC.

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Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

Cited by 5 publications

References 32 publications

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study

Deep learning model for predicting postoperative survival of patients with gastric cancer

A novel web-based dynamic prognostic nomogram for gastric signet ring cell carcinoma: a multicenter population-based study

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