ArticleFrontiers in oncology2023
Morphometric analysis of tumor microvessels for detection of hepatocellular carcinoma using contrast-free ultrasound imaging: A feasibility study.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 10 citations in OpenAlex.
- Microvascular Imaging of Hepatic Hemangiomas.Diagnostics (Basel, Switzerland) · 2025Article
- Quantitative microvessel orientation biomarkers derived from contrast free ultrasound imaging for cancer diagnosis.Scientific reports · 2025Article
- Role of Superb Microvascular Imaging (SMI) vascularity index values and vascularity patterns in the differential diagnosis of malignant liver lesions.Abdominal radiology (New York) · 2025Article
- Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification.Scientific reports · 2025Article
- Ultrasound-based quantitative microvasculature imaging for early prediction of response to neoadjuvant chemotherapy in patients with breast cancer.Breast cancer research : BCR · 2025Article
- NAFLD accelerates arterial perfusion in rabbit VX2 liver tumors: a sonoVue-based contrast-enhanced ultrasound study.Frontiers in oncology · 2025Article
- Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning.Scientific reports · 2024Article
- Article
- Background Noise Removal in Non-Contrast- Enhanced Ultrasound Microvasculature Imaging Using Combined Collaborative, Morphological, and Vesselness Filtering.IEEE access : practical innovations, open solutions · 2024Article
- Article
- Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach.Breast cancer research : BCR · 2023Article
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7 authors at 1 institution in 1 country.
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Abstract
Introduction: A contrast-free ultrasound microvasculature imaging technique was evaluated in this study to determine whether extracting morphological features of the vascular networks in hepatic lesions can be beneficial in differentiating benign and malignant tumors (hepatocellular carcinoma (HCC) in particular). Methods: A total of 29 lesions from 22 patients were included in this work. A post-processing algorithm consisting of clutter filtering, denoising, and vessel enhancement steps was implemented on ultrasound data to visualize microvessel structures. These structures were then further characterized and quantified through additional image processing. A total of nine morphological metrics were examined to compare different groups of lesions. A two-sided Wilcoxon rank sum test was used for statistical analysis. Results: In the malignant versus benign comparison, six of the metrics manifested statistical significance. Comparing only HCC cases with the benign, only three of the metrics were significantly different. No statistically significant distinction was observed between different malignancies (HCC versus cholangiocarcinoma and metastatic adenocarcinoma) for any of the metrics. Discussion: Obtained results suggest that designing predictive models based on such morphological characteristics on a larger sample size may prove helpful in differentiating benign from malignant liver masses.
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