ReviewFrontiers in oncology2025
Ultrasound elastography: advances and challenges in early detection of breast cancer.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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.
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Who cites it
9 citing papers in PubMed.
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
- Deep Learning-Based Inclusion Boundary Identification Using Wave Propagation in Optical Coherence Elastography.Journal of biophotonics · 2026Article
- MBUCF: a cross-fusion method for multimodal breast ultrasound based on multidimensional features.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Tumor microenvironment and mechanotransduction pathways: Novel targets and new directions for cancer therapy.Mechanobiology in medicine · 2026Review
- Bandgap-enabled ultrasound tissue marking by a biodegradable metastructured hydrogel implant.Science advances · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Development and multicenter external validation of an intratumoral and peritumoral ultrasound-based radiomics model for preoperative prediction of HER2 status in IHC 2 + breast cancer.European journal of medical research · 2025Article
- Multimodal fusion of ultrasound images using HXM net for breast cancer diagnosis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review explores recent advances in ultrasound elastography for breast cancer detection, focusing on technological innovations, clinical validation, and emerging challenges in early diagnosis. We analyze how modern elastographic techniques have evolved to address the critical need for accurate, non-invasive breast cancer screening and characterization. Recent methodological developments in ultrasound elastography have significantly enhanced its diagnostic capabilities, particularly in distinguishing malignant from benign breast lesions. We highlight breakthrough technologies including shear wave elastography, strain ratio measurements, and advanced quantitative methods that provide detailed mechanical characterization of breast tissue. The review specifically addresses how these techniques improve the detection of small, early-stage tumors and reduce false-positive rates in dense breast tissue. Artificial intelligence integration has transformed breast elastography workflow, introducing sophisticated pattern recognition and automated lesion characterization. The review also addresses current challenges, including the need for technical standardization, ensuring consistent reproducibility across different settings, managing economic costs, improving accessibility, and developing comprehensive education and training programs for healthcare providers. We analyze emerging solutions, including novel quality assurance protocols and adaptive imaging techniques that accommodate different breast tissue compositions. On summarizing and critically analyzing clinical evidence and technological developments, this review provides a comprehensive perspective on the current state and future directions of breast ultrasound elastography. The integration of advanced elastographic methods with artificial intelligence and standardized protocols promises to establish ultrasound elastography as an essential tool in early breast cancer detection, potentially improving patient outcomes through earlier intervention.
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Registered trials
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.