Evidence map›Paper›PMID 37284080›Full record

ArticleQuantitative imaging in medicine and surgery2023

Revised Thyroid Imaging Reporting and Data System (TIRADS): imitating the American College of Radiology TIRADS, a single-center retrospective study.

Fengping Liang, Xiang Li, Qiao Ji, Danni He, Mo Yang, Zuofeng Xu

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
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

9 citing papers in PubMed, 11 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Fengping Liang *Department of Medical Ultrasound, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Xiang Li *Department of Medical Ultrasound, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Qiao JiDepartment of Medical Ultrasound, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Danni HeDepartment of Medical Ultrasound, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Mo YangResearch Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Zuofeng XuDepartment of Medical Ultrasound, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Sun Yat-sen University · CNThe Seventh Affiliated Hospital of Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The incidence of thyroid lumps is more and more high in population, and most biopsies of thyroid nodules are benign. To develop a practical risk stratification system based on five ultrasound features to stratify the malignancy risk of thyroid neoplasms. Methods: This retrospective investigation enrolled 999 consecutive patients with 1,236 thyroid nodules who underwent ultrasound screening. Fine-needle aspiration and/or surgery was performed, and pathology results were obtained at the Seventh Affiliated Hospital of Sun Yat-sen University in Shenzhen, China, which is a tertiary referral center, from May 2018 to February 2022. Each thyroid nodule's score was calculated based on five ultrasound features: composition, echogenicity, shape, margin, and echogenic foci. Additionally, each nodule's malignancy rate was calculated. The chi-square test was used to test whether the malignancy rate was different among the three subcategories (scores of 4-6, 7-8, and 9 or more) of thyroid nodules. We proposed the revised Thyroid Imaging Reporting and Data System (R-TIRADS), and its sensitivity and specificity were compared to the two existing systems [the American College of Radiology TIRADS (ACR TIRADS) and the Korean Society of Thyroid Radiology TIRADS (K-TIRADS)]. Results: The final dataset consisted of 425 nodules from 370 patients. The malignancy rates of three subcategories [malignancy rate: 28.8% (scores from 4-6), 64.7% (scores from 7-8), and 84.2% (scores of 9 or more)] were significantly different (P<0.01). The unnecessary biopsy rates of the three systems (ACR TIRADS, R-TIRADS, and K-TIRADS) were 28.7%, 25.2%, and 14.8%, respectively. The R-TIRADS presented better diagnostic performance than the ACR TIRADS or K-TIRADS [area under the curve: 0.79 (95% CI: 0.74-0.83) Conclusions: The R-TIRADS enables radiologists to diagnose thyroid nodules efficiently, and the number of unnecessary fine-needle aspirations can be considerably reduced.

Indexed as

diagnosisthyroid imaging reporting and data system (TIRADS)thyroid noduleUltrasound (US)

Identifiers

PMID37284080
PMCPMC10240023
OpenAlexW4377102752

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.