SynthesisBMC cancer2020
Performance of ultrasonography screening for breast cancer: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.
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
46 citing papers in PubMed, 4 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- The value of artificial intelligence in ultrasound imaging for predicting molecular subtypes of breast cancer: a meta-analysis.Frontiers in oncology · 2026Pooled it
- The screening value of mammography for breast cancer: an overview of 28 systematic reviews with evidence mapping.Journal of cancer research and clinical oncology · 2025Pooled it
- Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women.BMC women's health · 2024Pooled it
- Supplemental Screening as an Adjunct to Mammography for Breast Cancer Screening in People With Dense Breasts: A Health Technology Assessment.Ontario health technology assessment series · 2023Pooled it
- Review
- Development and validation of an interpretable ultrasound radiomics model for benign and malignant classification of breast lesions: a multicenter large-sample study.Insights into imaging · 2026Article
- Article
- Feasibility of Using an AI System for Breast Ultrasonography Interpretation According to Clinical Expertise: Results of a Pilot Study.Journal of the Korean Society of Radiology · 2026Article
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- Multimodal deep learning for breast tumor classification: Integrating mammography and ultrasound for enhanced diagnostic accuracy.Journal of applied clinical medical physics · 2026Article
- Development and Evaluation of A PC-Compatible E-Learning System for Mammography and Breast Ultrasound.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Article
- Identification of the implementation influencing factors of the organized breast cancer screening in Ordos city: from caregivers' perspectives.BMC health services research · 2025Article
- YOLO AI model based on an automated breast volume scanner for the detection of benign and malignant breast lesions.Quantitative imaging in medicine and surgery · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Prediction of mammographic breast density based on clinical breast ultrasound images using deep learning: a retrospective analysis.Lancet regional health. Americas · 2025Article
- Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities.Cureus · 2025Review
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Improving breast ultrasonography education: the impact of AI-based decision support on the performance of non-specialist medical professionals.Ultrasonography (Seoul, Korea) · 2025Article
- Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation.Frontiers in oncology · 2025Article
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo investigate the performance of primary ultrasound (P-US) screening for breast cancer, and that of supplemental ultrasound (S-US) screening for breast cancer after negative mammography (MAM).
methodsElectronic databases (PubMed, Scopus, Web of Science, and Embase) were systematically searched to identify relevant studies published between January 2003 and May 2018. Only high-quality or fair-quality studies reporting any of the following performance values for P-US or S-US screening were included: sensitivity, specificity, cancer detected rate (CDR), recall rate (RR), biopsy rate (BR), proportion of invasive cancers among screening-detected cancers (ProIC), and proportion of node-negative cancers among screening-detected invasive cancers (ProNNIC).
resultsTwenty-three studies were included, including 12 studies in which S-US screening was used after negative MAM and 11 joint screening studies in which both primary MAM (P-MAM) and P-US were used. Meta-analyses revealed that S-US screening could detect 96% [95% confidential intervals (CIs): 82 to 99%] of occult breast cancers missed by MAM and identify 93% (95% CIs: 89 to 96%) of healthy women, with a CDR of 3.0/1000 (95% CIs: 1.8/1000 to 4.6/1000), RR of 8.8% (95% CIs: 5.0 to 13.4%), BR of 3.9% (95% CIs: 2.7 to 5.4%), ProIC of 73.9% (95% CIs: 49.0 to 93.7%), and ProNNIC of 70.9% (95% CIs: 46.0 to 91.6%). Compared with P-MAM screening, P-US screening led to the recall of significantly more women with positive screening results [1.5% (95% CIs:0.6 to 2.3%), P = 0.001] and detected significantly more invasive cancers [16.3% (95% CIs: 10.6 to 22.1%), P < 0.001]. However, there were no significant differences for other performance measures between the two screening methods, including sensitivity, specificity, CDR, BR, and ProNNIC.
conclusionsCurrent evidence suggests that S-US screening could detect occult breast cancers missed by MAM. P-US screening has shown to be comparable to P-MAM screening in women with dense breasts in terms of sensitivity, specificity, cancer detection rate, and biopsy rate, but with higher recall rates and higher detection rates for invasive cancers.
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.