Evidence map›Paper›PMID 39991881›Full record

ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2025

Comparative Evaluation of Superb Microvascular Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Differentiating Benign and Malignant Breast Masses.

Ahmet Yasin Yitik, Nuran Sabir, Sevda Yılmaz

Abstract readComparative Study
In one paragraph

Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Ahmet Yasin YitikDepartment of Radiology, Pamukkale University Medical Faculty Hospital, Denizli, Turkey.ORCID https://orcid.org/0000-0002-1793-7911
Nuran SabirDepartment of Radiology, Pamukkale University Medical Faculty Hospital, Denizli, Turkey.ORCID https://orcid.org/0000-0002-3412-7355
Sevda YılmazDepartment of General Surgery, Pamukkale University Medical Faculty Hospital, Denizli, Turkey.ORCID https://orcid.org/0000-0002-1309-0805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesOur study aims to compare the diagnostic performance of superb microvascular imaging (SMI) and dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating benign from malignant breast masses, using histopathological findings as the reference standard.

methodsThis prospective study was conducted from April 2022 to March 2024. A total of 112 breast lesions from 110 patients were evaluated using gray-scale ultrasonography, SMI, and dynamic contrast-enhanced MRI. The vascular index (VI) obtained during SMI examination and kinetic curve patterns from MRI were analyzed.

resultsHistopathological analysis revealed 62 benign and 50 malignant lesions. The VI showed a statistically significant difference between benign and malignant lesions, with a mean VI of 5.12 ± 4.66 in benign masses and 10.13 ± 5.48 in malignant masses (P < .001). The ROC analysis demonstrated an AUC of 0.79 for SMI with a VI cut-off value of 4.15, yielding a sensitivity of 92%, specificity of 60%, and accuracy of 74%. A statistically significant correlation was found between VI values and MRI contrast enhancement kinetic curve types (P < .05). MRI demonstrated superior diagnostic performance, with an AUC of 0.89 and sensitivity, specificity, and accuracy of 98, 80.65, and 88.39%, respectively.

conclusionsSMI, when used in conjunction with conventional ultrasonography and MRI, provides significant diagnostic value in differentiating benign from malignant breast masses. The study supports the potential integration of SMI into routine breast cancer diagnostic workflows, particularly in settings where MRI is less accessible.

Indexed as

Breast NeoplasmsContrast MediaImage EnhancementMagnetic Resonance ImagingMicrovesselsUltrasonography, MammaryAdultAgedBreastDiagnosis, DifferentialFemaleHumansMiddle AgedProspective StudiesReproducibility of ResultsSensitivity and SpecificityContrast Mediabreast cancerbreast imagingmagnetic resonance imagingsuperb microvascular imagingvascular index

Identifiers

PMID39991881
PMCPMC12067153

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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.