ArticleAJR. American journal of roentgenology2017
Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.
Article in AJR. American journal of roentgenology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.BMC medical imaging · 2023Trial
- Amide proton transfer-weighted magnetic resonance imaging for the diagnosis of triple-negative breast cancer: a comparative study with diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Time-dependent diffusion MRI for non-invasive differentiation of benign and malignant breast lesions and evaluation of proliferation index biomarkers.Breast cancer (Tokyo, Japan) · 2026Article
- An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.Nature communications · 2026Article
- Article
- Diagnostic performance of Kaiser score, ADC, and their combination for BI-RADS 4 breast lesions: subgroup and misclassification analysis.Frontiers in oncology · 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
- Nomogram for reducing unnecessary biopsies of breast lesions based on MRI and clinical features: a multi-center retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Reducing breast biopsy at MRI: comparison of minimum ADC cutoff and mean ADC cutoff identified by the ECOG-ACRIN A6702 multicenter trial.BMC cancer · 2025Article
- Idiopathic granulomatous lobular mastitis emulating breast carcinoma: a case report.Journal of medical case reports · 2025Article
- Reduction of biopsy rate in BI-RADS4 breast lesions: potential of an abbreviated advanced DWI protocol.European radiology · 2025Article
- Development and validation of an intratumoral-peritumoral deep transfer learning fusion model for differentiating BI-RADS 3-4 breast nodules.Gland surgery · 2025Article
- An Analysis of the Results of Breast Imaging Reporting and Data System 4 Lesions in Tertiary Care Center in South India.Indian journal of surgical oncology · 2025Article
- Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience.European radiology · 2025Article
- Third-look contrast-enhanced ultrasonography plus needle biopsy for differential diagnosis of magnetic resonance imaging-only detected breast lesions.Journal of medical ultrasonics (2001) · 2024Review
- Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study.Breast cancer research : BCR · 2024Article
- Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4).BMC medical imaging · 2023Article
- A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.Radiology. Artificial intelligence · 2023Article
- Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features.Gland surgery · 2023Article
- Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies.Frontiers in oncology · 2023Article
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Funding
Abstract
objectiveBI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND
methodsAll screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated.
resultsOf 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7).
conclusionCategory 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.
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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.