ArticleRadiology2018
ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.
Article in Radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed.
- Review
- Multimodal combined model integrating 2.5D deep learning and habitat radiomics for malignancy discrimination in ≤2 cm BI-RADS 4 breast lesions.Quantitative imaging in medicine and surgery · 2026Article
- Breathprints for Breast Cancer: Evaluating a Non-Invasive Approach to BI-RADS 4 Risk Stratification in a Preliminary Study.Cancers · 2026Article
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
- Development and validation of an integrated model combining deep learning, radiomics, and clinical and breast ultrasound features for Breast Imaging Reporting and Data System 4A lesion malignancy classification.Quantitative imaging in medicine and surgery · 2025Article
- Cost-effectiveness of contrast-enhanced breast MRI in suspicious mammographic microcalcifications.Insights into imaging · 2025Article
- Ultrafast Breast MRI: A Narrative Review.Journal of personalized medicine · 2025Review
- Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.Quantitative imaging in medicine and surgery · 2025Article
- Mammography Based Nomogram Integrating Radiomics and Clinical Features to Predict Benign or Malignant Regression of BI-RADS 4A Lesions at Follow-Up.International journal of women's health · 2025Article
- Novel study on the prediction of BI-RADS 4A positive lesions in mammography using deep learning technology and clinical factors.Quantitative imaging in medicine and surgery · 2024Article
- Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization.Pakistan journal of medical sciences · 2024Article
- The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model.La Radiologia medica · 2024Article
- Mucinous Carcinoma of the Breast: A Case Report.Cureus · 2024Article
- The benefits of contrast-enhanced ultrasound in the differential diagnosis of suspicious breast lesions.Frontiers in medicine · 2024Article
- Multiview deep learning networks based on automated breast volume scanner images for identifying breast cancer in BI-RADS 4.Frontiers in oncology · 2024Article
- A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.Radiology. Artificial intelligence · 2023Article
- Review
- Value of high-resolution full-field optical coherence tomography and dynamic cell imaging for one-stop rapid diagnosis breast clinic.Cancer medicine · 2023Article
- Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.Journal of digital imaging · 2023Article
- Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.BMC medical imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ
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.