Evidence map›Paper›PMID 40637817›Full record

ArticleEndocrine2025

Multiparametric ultrasound techniques are superior to AI-assisted ultrasound for assessment of solid thyroid nodules: a prospective study.

Yingying Li, Xinyang Li, Lin Yan, Jing Xiao, Zhen Yang, Mingbo Zhang, Yukun Luo

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Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper 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

7 authors.

Yingying LiMedical School of Chinese PLA, Beijing, China.
Xinyang LiDepartment of Ultrasound, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Lin YanDepartment of Ultrasound, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Jing XiaoDepartment of Ultrasound, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Zhen YangMedical School of Chinese PLA, Beijing, China.
Mingbo ZhangDepartment of Ultrasound, the First Medical Center, Chinese PLA General Hospital, Beijing, China. zhangmingbo@301hospital.com.cn.
Yukun LuoDepartment of Ultrasound, the First Medical Center, Chinese PLA General Hospital, Beijing, China. lyk_301@126.com.

Funding

Beijing Municipal Science & Technology Commission No. Z221100003522001
6 · The paper itself

Abstract

objectivesTo evaluate the diagnostic performance of multiparametric ultrasound (mpUS) and AI-assisted B-mode ultrasound (AI-US), and their potential to reduce unnecessary biopsies to B-mode for solid thyroid nodules.

methodsThis prospective study enrolled 226 solid thyroid nodules with 145 malignant and 81 benign pathological results from 189 patients (35 men and 154 women; age range, 19-73 years; mean age, 45 years). Each nodule was examined using B-mode, microvascular flow imaging (MVFI), elastography with elasticity contrast index (ECI), and an AI system. Image data were recorded for each modality. Ten readers with different experience levels independently evaluated the B-mode images of each nodule to make a "benign" or "malignant" diagnosis in both an unblinded and blinded manner to the AI reports. The most accurate ECI value and MVFI mode were selected and combined with the dichotomous prediction of all readers. Descriptive statistics and AUCs were used to evaluate the diagnostic performances of mpUS and AI-US.

resultsTriple mpUS with B-mode, MVFI, and ECI exhibited the highest diagnostic performance (average AUC = 0.811 vs. 0.677 for B-mode, p = 0.001), followed by AI-US (average AUC = 0.718, p = 0.315). Triple mpUS significantly reduced the unnecessary biopsy rate by up to 12% (p = 0.007). AUC and specificity were significantly higher for triple mpUS than for AI-US mode (both p < 0.05).

conclusionsCompared to AI-US, triple mpUS (B-mode, MVFI, and ECI) exhibited better diagnostic performance for thyroid cancer diagnosis, and resulted in a significant reduction in unnecessary biopsy rate. AI systems are expected to take advantage of multi-modal information to facilitate diagnoses.

Indexed as

Thyroid NoduleAdultAgedElasticity Imaging TechniquesFemaleHumansMaleMiddle AgedProspective StudiesThyroid GlandUltrasonographyYoung AdultArtificial intelligenceElastographyMicrovascular flow imagingThyroid nodulesUltrasonography

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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.