Evidence map›Paper›PMID 38265473›Full record

ArticleEuropean radiology2024

Evaluation of tumor microvasculature with 3D ultrasound localization microscopy based on 2D matrix array.

Changlu Zhang, Shuang Lei, Aiqing Ma, Bing Wang, Shuo Wang, Jiamei Liu, Dongqing Shang, Qi Zhang, Yongchuan Li, Hairong Zheng and 1 more

Abstract read
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In one paragraph

Article in European radiology, 2024. 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

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

1 citing paper in PubMed.

  1. Article
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.

Changlu ZhangInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.ORCID http://orcid.org/0000-0003-2382-649X
Shuang LeiSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China.
Aiqing MaNanomedicine and Nanoformulations Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, 518055, China.
Bing WangInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Shuo WangInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Jiamei LiuInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Dongqing ShangInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Qi ZhangInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yongchuan LiInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Hairong ZhengInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Teng MaInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. tteng.ma@siat.ac.cn.

Funding

Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense ZDSYS20180206180631473National Natural Science Foundation Grant of China 11774370National Natural Science Foundation Grant of China 61571431National Natural Science Foundation Grant of China 61971411National Natural Science Foundation Grant of China 81527901National Natural Science Foundation Grant of China 81827802National Natural Science Foundation Grant of China 81827807National Social Science Fund of China YJKYYQ20190077Shenzhen Polytechnic KQTD20170810160424889
6 · The paper itself

Abstract

objectiveEvaluation of tumor microvascular morphology is of great significance in tumor diagnosis, therapeutic effect prediction, and surgical planning. Recently, two-dimensional ultrasound localization microscopy (2DULM) has demonstrated its superiority in the field of microvascular imaging. However, it suffers from planar dependence and is unintuitive. We propose a novel three-dimensional ultrasound localization microscopy (3DULM) to avoid these limitations.

methodsWe investigated 3DULM based on a 2D array for tumor microvascular imaging. After intravenous injection of contrast agents, all elements of the 2D array transmit and receive signals to ensure a high and stable frame rate. Microbubble signal extraction, filtering, positioning, tracking, and other processing were used to obtain a 3D vascular map, flow velocity, and flow direction. To verify the effectiveness of 3DULM, it was validated on double helix tubes and rabbit VX2 tumors. Cisplatin was used to verify the ability of 3DULM to detect microvascular changes during tumor treatment.

resultsIn vitro, the sizes measured by 3DULM at 3 mm and 13 mm were 178

conclusionsThe effectiveness of 3DULM was verified in vitro and in vivo. Hence, 3DULM may have potential applications in tumor diagnosis, tumor treatment evaluation, surgical protocol guidance, and cardiovascular disease. CLINICAL RELEVANCE STATEMENT: 3D ultrasound localization microscopy is highly sensitive to microvascular changes; thus, it has clinical potential for tumor diagnosis and treatment evaluation. KEY POINTS: • 3D ultrasound localization microscopy is demonstrated on double helix tubes and rabbit VX2 tumors. • 3D ultrasound localization microscopy can reveal vessels about 30

Indexed as

Contrast MediaImaging, Three-DimensionalMicrovesselsAnimalsMicrobubblesMicroscopyNeoplasmsRabbitsUltrasonographyContrast MediaMicrobubbleMicrovasculatureUltrasound imaging

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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.