Evidence map›Paper›PMID 41184781›Full record

SynthesisBMC medical imaging2025

Performance of artificial intelligence-assisted ultrasound elastography in classifying benign and malignant breast tumors: a systematic review and meta-analysis.

Wenhan Hu, Xi Li, Yong Tang, Xinyue Mao, Chunmei Li, Yi Wang, Zhaohuan Li, Mingliang Zuo, Lixue Yin, Yan Deng and 1 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC medical imaging, 2025. 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

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

2 citing papers in PubMed.

  1. Article
  2. Review
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

11 authors.

Wenhan Hu *School of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, China.
Xi Li *School of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, China.
Yong TangSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xinyue MaoSchool of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, China.
Chunmei LiDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Yi WangDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Zhaohuan LiDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Mingliang ZuoDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Lixue YinDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Yan DengDepartment of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China. dengyan@med.uestc.edu.cn.
Liqiang DengDepartment of Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China. dengliqiang@med.uestc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecise benign and malignant breast tumors classification is essential for effective treatment planning and outcome prognostication. Medical imaging's capability to classify breast tumors has been greatly improved by the accelerated advancement of artificial intelligence (AI). This research presents a comprehensive evaluation of the efficiency of AI-assisted ultrasound elastography (UE) specifically applied to classify benign and malignant breast tumors for the first time.

methodsWe conducted extensive literature search in PubMed, Embase, IEEE, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang database, and China Biology Medicine disc (CBM) to identify relevant studies that applied or developed AI algorithms for classifying benign and malignant breast masses employing UE. We used bivariate mixed-effects model for statistical analysis, obtaining binary diagnostic accuracy data to generate pooled estimates (e.g., sensitivity and specificity). The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was applied to assess the methodological quality of the included research. Sensitivity analysis was conducted to verify the robustness of the findings, and Deeks' funnel plot was employed to examine potential publication bias. Meta-regression analysis was used to investigate the sources of heterogeneity. Clinical applicability was evaluated by Fagan nomogram.

resultsThe meta-analysis comprised sixteen relevant studies. Summary estimates indicated high diagnostic accuracy: the pooled sensitivity was 0.90 (95% CI: 0.85-0.94), the pooled specificity was 0.88 (0.81-0.93), the positive likelihood ratio (PLR) was 7.5 (4.7-11.9), and the negative likelihood ratio (NLR) was 0.11 (0.07-0.18). The diagnostic odds ratio (DOR) was 67 (33-137), and the area under the summary receiver operating characteristic curve (AUC) was 0.95 (0.93-0.97).

conclusionAI-assisted UE demonstrates outstanding performance in benign and malignant breast tumors classification. This study was registered with PROSPERO (CRD42024590031).

Indexed as

Artificial IntelligenceBreast NeoplasmsElasticity Imaging TechniquesUltrasonography, MammaryAlgorithmsFemaleHumansSensitivity and SpecificityArtificial intelligenceBreast tumorMeta-analysisUltrasound elastography

Identifiers

PMID41184781
PMCPMC12581291

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.