ArticleBMC medical imaging2020
Prospective evaluation of contrast-enhanced ultrasound of breast BI-RADS 3-5 lesions.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 42 citations in OpenAlex.
- Effect of Ultrasound Combined with Microbubbles on Blood Perfusion in Invasive Breast Cancer-A Prospective Clinical Trial.Technology in cancer research & treatmentTrial
- An explainable machine learning framework utilizing ultrasound radiomics for the preoperative differentiation between granulomatous lobular mastitis and breast cancer.Frontiers in oncology · 2026Article
- Exploring the Application of Contrast-Enhanced Ultrasound in ACR BI-RADS 4A Lesions (≤ 2 cm): A Prospective Multicenter Study in China.The breast journal · 2026Article
- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.BMC cancer · 2025Article
- Diagnostic test of conventional ultrasonography combined with contrast-enhanced ultrasound in the subcategorization of suspicious Breast Imaging-Reporting and Data System (BI-RADS) 4 breast lesions.Translational cancer research · 2025Article
- Review
- Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions.Journal of medical ultrasonics (2001) · 2024Review
- The benefits of contrast-enhanced ultrasound in the differential diagnosis of suspicious breast lesions.Frontiers in medicine · 2024Article
- Ceusia-Breast: computer-aided diagnosis with contrast enhanced ultrasound image analysis for breast lesions.BMC medical imaging · 2023Article
- Value of ultrasound BI‑RADS classification in preoperative evaluation of the ultrasound‑guided Mammotome‑assisted minimally invasive resection of breast masses: A retrospective analysis.Experimental and therapeutic medicine · 2023Article
- Evaluation of Two-Year Follow-Up of Patients with BI-RADS 3 Breast Ultrasound Lesions in a Single Private Ultrasound Study.Advanced biomedical research · 2023Article
- Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application.Cancer science · 2022Article
- Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics.Journal of ultrasound · 2022Article
- The clinical value of conventional ultrasound combined with contrast-enhanced ultrasound in the evaluation of BI-RADS 4 lesions detected by magnetic resonance imaging.The British journal of radiology · 2022Article
- Contrast-enhanced ultrasound of breast tumors: an initial experience.Acta radiologica open · 2022Article
- Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2022Article
- Differentiation Between Granulomatous Lobular Mastitis and Breast Cancer Using Quantitative Parameters on Contrast-Enhanced Ultrasound.Frontiers in oncology · 2022Article
- Ultrasound super-resolution imaging for differential diagnosis of breast masses.Frontiers in oncology · 2022Article
- Contrast-enhanced ultrasound of granular cell tumor in breast: A case report with review of the literature.Frontiers in oncology · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo determine the benefit of contrast-enhanced ultrasound (CEUS) in the assessment of breast lesions.
methodsA standardized contrast-enhanced ultrasound was performed in 230 breast lesions classified as BI-RADS category 3 to 5. All lesions were subjected to qualitative and quantitative analysis. MVI (MicroVascular Imaging) technique was used to derive qualitative analysis parameters; blood perfusion of the lesions was assessed (perfusion homogeneity, type of vascularization, enhancement degree). Quantitative analysis was conducted to estimate perfusion changes in the lesions within drawn regions of interest (ROI); parameters TTP (time to peak), PI (peak intensity), WIS (wash in slope), AUC (area under curve) were obtained from time intensity (TI) curves. Acquired data were statistically analyzed to assess the ability of each parameter to differentiate between malignant and benign lesions. The combination of parameters was also evaluated for the possibility of increasing the overall diagnostic accuracy. Biological nature of the lesions was verified by a pathologist. Benign lesions without histopathological verification (BI-RADS 3) were followed up for at least 24 months.
resultsOut of 230 lesions, 146 (64%) were benign, 67 (29%) were malignant, 17 (7%) lesions were eliminated. Malignant tumors showed statistically significantly lower TTP parameters (sensitivity 77.6%, specificity 52.7%) and higher WIS values (sensitivity 74.6%, specificity 66.4%) than benign tumors. Enhancement degree also proved to be statistically well discriminating as 55.2% of malignant lesions had a rich vascularity (sensitivity 89.6% and specificity 48.6%). The combination of quantitative analysis parameters (TTP, WIS) with enhancement degree did not result in higher accuracy in distinguishing between malignant and benign breast lesions.
conclusionsWe have demonstrated that contrast-enhanced breast ultrasound has the potential to distinguish between malignant and benign lesions. In particular, this method could help to differentiate lesions BI-RADS category 3 and 4 and thus reduce the number of core-cut biopsies performed in benign lesions. Qualitative analysis, despite its subjective element, appeared to be more beneficial. A combination of quantitative and qualitative analysis did not increase the predictive capability of CEUS.
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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.