ArticleInternational journal of general medicine2021
Development and Validation of an Ultrasonic Diagnostic Model for Differentiating Follicular Thyroid Carcinoma from Follicular Adenoma.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Dynamic Predictors for Malignancy Risk of Follicular Thyroid Neoplasms: A Real-World, 10-Year Cohort Study.Cancer medicine · 2026Article
- Independent predictors and ultrasound-based risk stratification for follicular thyroid tumors: a multicenter retrospective study.World journal of surgical oncology · 2025Article
- Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.Journal of imaging informatics in medicine · 2024Article
- Development and validation of a predictive model for assessing the risk of follicular carcinoma in thyroid nodules identified as suspicious by intraoperative frozen section.Frontiers in endocrinology · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundHigh-resolution ultrasound is the first choice for the diagnosis of thyroid nodules, but it is still difficult to distinguish between follicular thyroid carcinoma (FTC) and follicular adenoma (FA). Our research aimed to develop and validate an ultrasonic diagnostic model for differentiating FTC from FA.
methodsThis study retrospectively analyzed 196 patients who were diagnosed as FTC (n=83) and FA (n=113). LASSO regression analysis was used to screen clinical and ultrasonic features. Multivariate logistic regression analysis was used to establish the ultrasonic diagnostic model of FTC. Nomogram was used for the visualization of diagnostic models. C-index, ROC, and calibration curves analysis were used to evaluate the accuracy of the diagnostic model. Decision curve analysis (DCA) was used to evaluate the net benefits of the ultrasonic diagnostic model for FTC diagnosis under different threshold probabilities. The bootstrap method was used to verify the ultrasonic diagnostic model.
resultsAfter Lasso regression analysis, 10 clinical and ultrasonic features were used to construct the ultrasonic diagnostic model of FTC. The C-index and AUC of the model were 0.868 and 0.860, respectively. DCA showed that the ultrasonic model had good clinical application value. The C-index in the validation group was 0.818, which was close to the C-index in the model.
conclusionUltrasonic diagnostic model constructed with 10 clinical and ultrasonic features can better distinguish FTC from FA.
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