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

Sep 21, 2026

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) show increasing incidence, yet prognosis remains difficult due to complex and inconsistent treatment. This study constructed and validated nomograms that incorporated treatment data to predict overall survival (OS) in patients with GEP-NETs. Methods: Patients were identified from SEER databases, using Cox regression with AIC-based selection to determine independent prognostic factors and estimate OS via hazard ratios and 95% confidence intervals. Results: Key OS predictors included demographic factors, tumor characteristics, and treatment modalities, with improved discrimination versus the AJCC 7th staging system. Conclusion: A treatment-data nomogram may better estimate individual OS and support clinical decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Background

Methods

Results

Internal and external validation

Comparison with AJCC staging and decision curve analysis

Conclusion

Keywords

’Retrospective Cohort Study A nomogram incorporating treatment data for predicting overall survival ingastroenteropancreatic neuroendocrine tumors: a population-based cohort study ZenghongWu, MD, Guochen Shang, MD, Kun Zhang, MD, Weijun Wang, MD, Mengke Fan, MD, Rong Lin, MD, PhD* Background: Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumours (GEPNETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEPNETs 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 = 7564) were randomly divided into training (3782 patients) and testing sets (3782 patients) . In the univariate analysis, sex, age, race, tumour location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumour size were found to be signiﬁcantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumour 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. The authors highly recommend to use their nomogram to evaluate individual risks based on different clinical features of GEPNETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life. Keywords: Decision curve analysis, gastroenteropancreatic neuroendocrine tumours, nomogram, overall survival Background The gastroenteropancreatic neuroendocrine tumours (GEPNETs) are class of heterogeneous rare tumours, origin from the diffuse neuroendocrine system of the pancreas and gastrointestinal tract. Over the last few decades, the annual global incidence of GEP-NETs has steadily increased. The projected prevalence of NETs in the US population in 2014 was 171 321[1] . Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Z.W., G.S are co-ﬁrst authors. Z.W., R.L are co-correspondence authors. Sponsorships or competing interests that may be relevant to content are disclosed at the end of

FAQs

What problem does the study address for GEP-NET patients?

The study targets the difficulty of assessing prognosis for patients with gastroenteropancreatic neuroendocrine tumors due to complex and inconsistent treatments.

How were independent prognostic factors selected for the nomogram?

Multivariate Cox regression was performed using the minimum Akaike information criterion (AIC) to identify independent prognostic factors and evaluate relationships with overall survival using hazard ratios and 95% confidence intervals.

How did the nomogram perform compared with the AJCC 7th staging system?

The nomogram showed better prediction performance than AJCC staging, with favorable C-index values and decision curve analysis indicating more clinical net benefit across a wider threshold probability range.

Sep 21, 2026

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