ArticleDiscover oncology2022
A novel diagnostic model for insulinoma.
Article in Discover oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Searching for New Biomarkers of Neuroendocrine Tumors: A Comparative Analysis of Chromogranin A and Inflammatory Cytokines in Patients with Neuroendocrine Tumors.Current oncology (Toronto, Ont.) · 2024Article
- Distinguishing Insulinoma From Reactive Hypoglycemia: Characteristic Glycemia Patterns During the 5-Hour Oral Glucose Tolerance Test.AACE endocrinology and diabetesArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The aim is to describe a simple and feasible model for the diagnosis of insulinoma. This retrospective study enrolled 37 patients with insulinoma and 44 patients with hypoglycemia not due to insulinoma at the First Affiliated Hospital of Guangxi Medical University. General demographic and clinical characteristics; hemoglobin A1c (HbA1c), insulin and C-peptide concentrations; and the results of 2-h oral glucose tolerance tests (OGTT) were recorded, and a logistic regression model predictive of insulinoma was determined. Body mass index (BMI), HbA1c concentration, 0-h C-peptide concentration, and 0-h and 1-h plasma glucose concentrations (P < 0.05 each) were independently associated with insulinoma. A regression prediction model was established through multivariate logistics regression analysis: Logit p = 7.399+(0.310 × BMI) - (1.851 × HbA1c) - (1.467 × 0-h plasma glucose) + (1.963 × 0-h C-peptide) - (0.612 × 1-h plasma glucose). Using this index to draw a receiver operating characteristic (ROC) curve, the area under the curve (AUC) was found to be 0.957. The optimal cut-off value was - 0.17, which had a sensitivity of 89.2% and a specificity of 86.4%. Logit P ≥ - 0.17 can be used as a diagnostic marker for predicting insulinoma in patients with hypoglycemia.
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Registered trials
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