Article ; Online: Diagnostic performance of the thyroid imaging reporting and data system improved by color-coded acoustic radiation force pulse imaging.
Journal of X-ray science and technology
2023 Volume 31, Issue 3, Page(s) 511–523
Abstract: Objective: To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules.: Methods: Images including 189 thyroid nodules were collected as ... ...
Abstract | Objective: To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules. Methods: Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves. Results: Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880. Conclusions: Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone. |
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MeSH term(s) | Humans ; Thyroid Nodule/diagnostic imaging ; Retrospective Studies ; Ultrasonography/methods ; Elasticity Imaging Techniques ; Acoustics |
Language | English |
Publishing date | 2023-03-02 |
Publishing country | Netherlands |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2012019-9 |
ISSN | 1095-9114 ; 0895-3996 |
ISSN (online) | 1095-9114 |
ISSN | 0895-3996 |
DOI | 10.3233/XST-221359 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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