Evidence map›Paper›PMID 37554760›Full record

ArticleFrontiers in endocrinology2023

Predictors and a prediction model for positive fine needle aspiration biopsy in C-TIRADS 4 thyroid nodules.

Zhijie Yang, Xin Gao, Lu Yang

Abstract read
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Article in Frontiers in endocrinology, 2023. 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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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Zhijie YangDepartment of Breast and Thyroid Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xin GaoDepartment of Breast and Thyroid Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Lu YangDepartment of Breast and Thyroid Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To screen out the predictors and establish a prediction model of positive fine needle aspiration biopsy (FNAB) in the Chinese Guidelines for Malignant Risk Stratification of Thyroid Nodule Ultrasound (C-TIRADS) 4 thyroid nodules, and this nomogram can help clinicians evaluate the risk of positive FNAB and determine if FNAB is necessary. Methods: We retrospectively analyzed data from 547 patients who had C-TIRADS 4 thyroid nodules and underwent fine-needle aspiration biopsy (FNAB) at the Second Affiliated Hospital of Chongqing Medical University between November 30, 2021 and September 5, 2022. Patients who met our inclusion criteria were divided into two groups based on positive or negative FNAB results. We compared their ultrasound (US) features, BRAF V600E status, thyroid function, and other general characteristics using univariate and multivariate logistic regression analyses to identify independent predictors. These predictors were then used to construct a nomogram. The calibration plot, area under the curve (AUC), and decision curve analysis were employed to evaluate the calibration, discrimination, and clinical utility of the prediction model. Results: Out of 547 patients, 39.3% (215/547) had a positive result on fine-needle aspiration biopsy (FNAB), while 60.7% (332/547) had a negative result. Univariate logistic regression analysis revealed no significant differences in TPOAb, TgAb, TSH, Tg, nodule location, sex, or solid status between the two groups (P>0.05). However, age, nodule size, internal or surrounding blood flow signal, microcalcifications, aspect ratio, morphology, and low echo showed significant differences (P<0.05). Multivariate logistic regression analysis was conducted to explore the correlation between potential independent predictors. The results showed that only age (OR=0.444, 95% Cl=0.296~0.666, P<0.001), low echo (OR=3.549, 95% Cl=2.319~5.432, P<0.001), microcalcifications (OR=2.531, 95% Cl=1.661~3.856, P<0.001), aspect ratio (OR=3.032, 95% Cl=1.819~5.052, P<0.001), and morphology (OR=2.437, 95% Cl=1.586~3.745, P<0.001) were independent predictors for a positive FNAB. These variables were used to construct a prediction nomogram. An ROC curve analysis was performed to assess the accuracy of the nomogram, and AUC=0.793, which indicated good discrimination and decision curve analysis demonstrated clinical significance within a threshold range of 14% to 91%. Conclusion: In conclusion, 5 independent predictors of positive FNAB, including age (≤45 years old), low echo (yes), microcalcifications (yes), aspect ratio (>1) and morphology (irregular), were identified. A nomogram was established based on the above 5 predictors, and the nomogram can be used as a complementary basis to help clinicians make decisions on FNAB of C-TI-RADS 4 thyroid nodules.

Indexed as

Thyroid NeoplasmsThyroid NoduleBiopsy, Fine-NeedleHumansMiddle AgedRetrospective StudiesUltrasonography95% Cl=0.296~0.666C-TIRADSfine needle aspiration biopsylow echo (OR=3.549)prediction model 444predictorthyroid nodules

Identifiers

PMID37554760
PMCPMC10405816

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