Evidence map›Paper›PMID 40533698›Full record

ArticleInsights into imaging2025

Differentiation of benign and malignant breast lesions by ultrasound localization microscopy.

Jia Li, Cong Wei, Tao Ying, Yan Liu, Ronghui Wang, Maoyao Li, Chao Feng, Di Sun, Yuanyi Zheng

Abstract read
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

Jia Li *Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Cong Wei *Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tao YingDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yan LiuDepartment of Ultrasound in Medicine, Dali Bai Autonomous Prefecture People's Hospital, Yunnan, China.
Ronghui WangDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Maoyao LiDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chao FengDivision of andrology, Department of reproductive medicine, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Di SunDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. Sundy316@163.com.
Yuanyi ZhengDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhengyuanyi@163.com.ORCID http://orcid.org/0000-0002-1328-0641

Funding

This study was supported by the Key Program of the National Natural Science Foundation of China No. 82030050This study was supported by the Key Program of the National Natural Science Foundation of China No. T2394534
6 · The paper itself

Abstract

objectiveWe investigated the role of ultrasound localization microscopy (ULM) qualitative and quantitative parameters in distinguishing benign from malignant breast lesions.

methodsThe ULM qualitative and quantitative parameters of breast lesions were recorded. A receiver operating characteristic (ROC) curve was applied to assess the diagnostic performance of ULM. Intra- and inter-operator reliabilities of quantitative parameters were assessed.

resultsThirty-one breast lesions were verified by pathologic results, 14 of which were benign and 17 were malignant. Benign lesions were associated with dot-like, line-like, or branch-like patterns (93% vs 6%), whereas malignant lesions were associated with chaotic patterns (94% vs 7%) (p < 0.001). The microvasculature morphology had an area under the curve (AUC) of 0.935, a sensitivity of 94.1%, and a specificity of 92.9%. The microvasculature density, mean diameter, largest diameter, and max tortuosity of malignant lesions were significantly greater than those of benign lesions (p < 0.05, p < 0.001, p < 0.001, p < 0.05). The microvasculature mean flow velocity of benign lesions was significantly greater than that of malignant lesions (p < 0.05). For the quantitative parameters, the AUC was highest for the microvasculature's largest diameter (0.962), with a sensitivity of 88.2% and a specificity of 92.9%. The intra- and inter-operator reliabilities of quantitative parameters were excellent (ICC greater than 0.90).

conclusionsULM is useful for distinguishing benign from malignant breast lesions. ULM can offer a new diagnostic method for breast lesions, which deserves further research. CRITICAL RELEVANCE STATEMENT: This study suggests that ULM is a new technology with super-resolution that is helpful for distinguishing benign from malignant breast lesions.

trial registrationChiCTR, ChiCTR2100048361. Registered 6 July 2021, https://www.chictr.org.cn/ . KEY POINTS: ULM is an emerging technology that can detect highly detailed networks of microvasculature. Microvasculature morphology based on ULM can be a good indicator for the differential diagnosis of breast lesions. Among quantitative parameters extracted from ULM, microvasculature largest diameter was the best for the classification of breast lesions.

Indexed as

Breast lesionsContrast-enhanced ultrasoundMicrovasculatureUltrasound localization microscopy

Identifiers

PMID40533698
PMCPMC12176706

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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 Socratic graph.