Evidence map›Paper›PMID 36495391›Full record

ArticleEndocrine2023

Predicting malignancy in thyroid nodules based on conventional ultrasound and elastography: the value of predictive models in a multi-center study.

Ying Zhang, Qiong-Yi Huang, Chang-Jun Wu, Qi Chen, Chun-Juan Xia, Bo-Ji Liu, Yun-Yun Liu, Yi-Feng Zhang, Hui-Xiong Xu

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Endocrine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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

9 authors.

Ying Zhang *Center of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Qiong-Yi Huang *Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 200072, Shanghai, China.
Chang-Jun WuDepartment of Ultrasound, The first Affifiliated Hospital of Harbin Medical University, 150007, Harbin, China.
Qi ChenDepartment of Ultrasound, The first Affifiliated Hospital of Harbin Medical University, 150007, Harbin, China.
Chun-Juan XiaDepartment of Ultrasound, The second Affifiliated Hospital of Kunming Medical University, 650106, Kunming, China.
Bo-Ji LiuCenter of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Yun-Yun LiuCenter of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Yi-Feng ZhangCenter of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China. 27763289@qq.com.ORCID 0000-0003-0789-322X
Hui-Xiong XuShanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clinical Research Center for Interventional Medicine, 200072, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to establish predictive models based on features of Conventional Ultrasound (CUS) and elastography in a multi-center study to determine appropriate preoperative diagnosis of malignancy in thyroid nodules with different risk stratification based on 2017 Thyroid Imaging Reporting and Data System by the American College of Radiology (ACR TI-RADS) guidelines.

methodsFive hundred forty-eight thyroid nodules from three centers pathologically confirmed by the cytology or histology were retrospectively enrolled in the study, which were examined by CUS and elastography before fine needle aspiration (FNA) and surgery. Characteristics of CUS of thyroid nodules were reviewed according to 2017 ACR TI-RADS. Binary logistic regression analysis was used to develop the prediction models based on the different risk stratification of CUS features and elastography which were statistically significant. Values of predictive models were evaluated regarding the discrimination and calibration.

resultsBinary logistic regression showed that patients' age, taller-than-wider, lobulated or irregular boundary, extra-thyroid extension, microcalcification and the elastic parameter of Virtual touch tissue imaging quantification (VTIQ) max were independent predictors for thyroid malignancy (p < 0.05) in the ACR model and showed the area under the curve (AUC) in training (0.912) and validation cohort (internal and external: 0.877 vs 0.935). Predictive models showed predictors in ACR TR4 and TR5 for malignancy and diagnostic performance of AUC in training, internal and external validation cohort respectively: the VTIQ max (p < 0.001) with AUC of 0.809 vs 0.842 vs 0.705 and the age, taller than wide, VTIQ max variables with AUC of 0.859 vs 0.830 vs 0.906 in validation cohort. All predictive models have better calibration capabilities (p > 0.05).

conclusionsPredictive models combined CUS and elastography features would aid clinicians to make appropriate preoperative diagnosis of thyroid nodules among different risk stratification. The elastography parameter of VTIQ max has the priority in distinguishing thyroid malignancy with moderately suspicious (ACR TR4).

Indexed as

Elasticity Imaging TechniquesThyroid NeoplasmsThyroid NoduleHumansRetrospective StudiesUltrasonographyBinary logistic regressionConventional UltrasoundElastographyPredictionThyroid nodule

Identifiers

PMID36495391

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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.