ArticleDiscover oncology2025
Development and validation of nomograms for distant metastasis and prognosis of small intestinal neuroendocrine tumors.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSmall 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.
methodsThis 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.
resultsA 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.
conclusionIn 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.
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