ReviewRadiology2023
Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.
Review in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05460975 (Association of Sonographic Glandular Tissue Component With Breast Cancer Risk Among Women With Dense Breasts), which is not on this map. Cited by 50 papers, 2 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.
Association of Sonographic Glandular Tissue Component With Breast Cancer Risk Among Women With Dense Breasts: A Prospective, Multinational Validation Study
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.Journal of the American College of Radiology : JACR · 2024Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Factors Associated with False-Positive Marks and False-Negative Cancers on Automated Breast Ultrasound Using an Artificial Intelligence-Based Computer-Aided Detection System.Journal of imaging informatics in medicine · 2026Article
- Comparison of Artificial Intelligence Detection and Short-Term Breast Cancer Risk Models within and beyond Their Intended Use in Mammography Screening.Radiology. Imaging cancer · 2026Article
- From pixels to pathology: how artificial intelligence mammographic risk scores capture tumor biology through imaging.European radiology · 2026Article
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- BI-RADS v2025 for breast ultrasound: key updates and Asian perspectives.Ultrasonography (Seoul, Korea) · 2026Article
- Automated calculation of background parenchymal enhancement as a biomarker of treatment responses and recurrence-free survival in breast cancer.Breast cancer research and treatment · 2026Article
- Advancements in Developing an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: Synthesizing a Signal-dependent Noise Stochastic Process.bioRxiv : the preprint server for biology · 2026Article
- Mammographic radiomics and breast density for predicting PD-L1 expression in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Article
- ABUS-based glandular tissue component classification for breast cancer risk prediction in Chinese women with dense breasts: a retrospective study.BMC medical imaging · 2026Article
- Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Ultrasound-based radiomics model for predicting axillary lymph node metastasis of breast cancer.BMC medical imaging · 2025Article
- Associations of early-adulthood adiposity with breast parenchymal tissue complexity within premenopausal women.Breast cancer research : BCR · 2025Article
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 2025Article
- Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records.Scientific reports · 2025Article
- Deep Learning-Based Recurrence Prediction in HER2-Low Breast Cancer: Comparison of MRI-Alone, Clinicopathologic-Alone, and Combined Models.Diagnostics (Basel, Switzerland) · 2025Article
- Stratifying Breast Lesion Risk Using BI-RADS: A Correlative Study of Imaging and Histopathology.Medicina (Kaunas, Lithuania) · 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
7 authors at 1 institution in 1 country.
Funding
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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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.