ReviewBioengineering (Basel, Switzerland)2024
Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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.
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.
Who cites it
36 citing papers in PubMed, 55 citations in OpenAlex.
- Microwave breast imaging: A review of clinical potential and technological advances.World journal of methodology · 2026Review
- Review
- Diagnostic Performance and Geographic Variation in Mammography Services: A Nine-Year Audit from Southern Jordan.Diagnostics (Basel, Switzerland) · 2026Article
- Non-Invasive Diagnosis of Early Breast Cancer: Current and Emerging Liquid Biopsy Biomarkers.Cancers · 2026Review
- Liquid crystal thermography for breast cancer detection: principles, current evidence, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Benchmarking YOLOs in breast ultrasound lesion segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer.NPJ digital medicine · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Favoring flat minima improves generalization of transfer-learned models for breast ultrasound tumor classification.Scientific reports · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- A multi-scale hybrid ResNet-transformer with distance-aware learning for interpretable BI-RADS mammographic classification.Scientific reports · 2026Article
- Construction of an ultrasound feature-based diagnostic model for predicting triple-negative breast cancer using 108 machine learning algorithm combinations.Journal of clinical imaging science · 2026Article
- Application of YOLO-v7 and YOLO-v8 Transfer Learning Models in Breast Lesion Classification and DiagnosisCurrent medical imaging · 2026Article
- Predicting radiological regression in benign breast hyperplasia: the prolactin-to-estradiol ratio as a prognostic biomarker.Frontiers in medicine · 2026Article
- Development and Validation of a Combined 2D-US and CDUS Nomogram for Differentiating Benign and Malignant Breast Masses: A Retrospective Study.International journal of women's health · 2026Article
- Discrimination of benign and malignant non-mass breast lesions using ultrasound radiomics with machine learning models.Journal of applied clinical medical physics · 2026Article
- Article
- Multi-site observational evaluation of Breast AI™-supported ultrasound risk stratification in breast cancer assessment pathways.PloS one · 2026Observational
- Clinically Focused Computer-Aided Diagnosis for Breast Cancer Using SE and CBAM with Multi-Head Attention.Tomography (Ann Arbor, Mich.) · 2025Article
- Screening, docking, and molecular dynamics analysis of Mitragyna speciosa (Korth.) compounds for targeting HER2 in breast cancer.Current research in structural biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.
Indexed as
Identifiers
What OpenQuestion holds
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.