ArticleFrontiers in endocrinology2026
Development and validation of a multimodal predictive model based on clinical, biochemical, and quantitative dual-energy CT parameters: for predicting the benignity and malignancy of thyroid nodules.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to develop and validate a clinical prediction model integrating clinical characteristics, biochemical markers, and quantitative dual-energy CT (DECT) parameters to differentiate malignant from benign thyroid nodules. Methods: This retrospective study included 172 patients with thyroid nodules (87 malignant and 85 benign). All patients underwent non-contrast and dual-phase contrast-enhanced dual-energy CT (DECT) of the thyroid. Candidate features for model development included clinical variables (sex and age), biochemical markers (CEA, TPOAb, FT4, TSH, FT3, TRAb, Tg, CT, and TgAb), and spectral CT-derived quantitative parameters (thyroid nodule volume [TV], spectral curve slope, effective atomic number, calcium, hydroxyapatite [HAP], iodine concentration, and ICDNR). The patients were randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. A predictive model was then developed and internally validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) to assess discrimination, calibration, and clinical utility. Results: Multivariable logistic regression identified age (OR = 0.93, Conclusion: In this study, we successfully developed a multimodal predictive model integrating clinical, biochemical, and spectral CT-derived quantitative features, which demonstrated excellent diagnostic accuracy for thyroid nodules. This non-invasive and objective tool may improve risk stratification, reduce unnecessary interventions, and support personalized patient management.
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