Development and validation of nomograms for distant metastasis and prognosis of small intestinal neuroendocrine
Changyan Xu1, Yuxi Feng2, Jinyi Xu1
1Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Background:
Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose early and are associated with a poor prognosis due to distant metastasis (DM). This study aims to develop and validate two prediction models to predict risk of DM and prognosis of SI-NETs patients with DM.
Methods:
This study included patients diagnosed with SI-NETs from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM. A nomogram was developed using these factors to predict the risk of DM. Separately, for prognostic modeling, patients with DM were selected. Candidate prognostic variables were first screened using the least absolute shrinkage and selection operator (LASSO) Cox regression to reduce dimensionality and avoid overfitting. Variables retained by LASSO were then entered into a multivariable Cox proportional hazards model, and only those with a significance level of P < 0.05 were considered independent prognostic factors and used to construct the final prognostic nomogram. Both nomograms were rigorously validated using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). Kaplan-Meier analysis was employed to evaluate survival differences between risk groups stratified by the prognostic nomogram.
Results:
A total of 4,046 patients with SI-NETs were enrolled in this study, of whom 882 had DM at initial diagnosis. Primary site, grade, histological type, T stage, N stage, and tumor size were independent predictive factors of DM (p < 0.05). Sex, age, grade, histological type, surgery and chemotherapy were independent risk factors for prognosis in SI-NETs patients with DM (p < 0.05). The nomogram models demonstrated robust accuracy in predicting both DM risk and prognostic outcomes.
Conclusion:
In conclusion, we constructed a new DM risk nomogram model and a new prognostic nomogram model for SI-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.
