Evidence map›Paper›PMID 39175708›Full record

ArticleGland surgery2024

Combing superb microvascular imaging with shear wave elastography for risk stratification of Thyroid Imaging Reporting and Data System (TI-RADS) 4 thyroid nodules.

Qiyang Chen, Minxia Hu, Feifan Bao, Hanxue Zhao

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Article in Gland surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Qiyang ChenDepartment of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-7007-3326
Minxia HuDepartment of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Feifan BaoDepartment of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Hanxue ZhaoDepartment of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-5781-9779

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: It is difficult to accurately assess the risk of Thyroid Imaging Reporting and Data System (TI-RADS) 4 thyroid nodules due to the overlap of benign and malignant conventional ultrasound (US) features of nodules. To reduce unnecessary needle biopsies and assist clinical decision-making, this study established a dynamic nomogram incorporating superb microvascular imaging (SMI) and shear wave elastography (SWE) for the risk evaluation of TI-RADS 4 thyroid nodules. Methods: A total of 248 patients who underwent US, SMI, and SWE with cytological or histopathological results were included in this retrospective study, and were randomly divided into training (174 patients) and verification (74 patients) cohorts. The clinical characteristics and US, SMI, and SWE features of patients were analyzed in the training cohort. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to screen parameters and construct dynamic nomogram. The receiver operating characteristic (ROC) curves, calibration curve, and decision curve were used to evaluate the performance of the nomogram. Results: A dynamic nomogram was constructed based on age [odds ratio (OR) =0.954; P=0.005] , shape (OR =0.345; P=0.041), SMI (OR =9.511; P<0.001), and SWE (OR =3.670; P=0.001). The nomogram showed excellent discrimination both in the training [area under the curve (AUC): 0.848; 95% confidence interval (CI): 0.784-0.911] and validation (AUC: 0.862; 95% CI: 0.780-0.944) cohorts, and better than US, SMI, and SWE alone in all cohorts (P<0.05). The Nomo-score of each patient was calculated and the cut-off value was 0.607 which can be used to distinguish high-risk and low-risk patients. Conclusions: The SMI and SWE show added predictive value on risk stratification in patients with TI-RADS 4 thyroid nodules and a dynamic nomogram was constructed to screen high-risk individuals and assist the clinical decision-making.

Indexed as

risk stratificationshear wave elastography (SWE)Superb microvascular imaging (SMI)thyroid nodule

Identifiers

PMID39175708
PMCPMC11336785

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.