ReviewInternational journal of breast cancer2015
A Review on Automatic Mammographic Density and Parenchymal Segmentation.
Review in International journal of breast cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 53 citations in OpenAlex.
- Zero-Shot Domain Adaptation of 2D Mammography Lesion Detection Model for 3D Breast CT Malignancy Detection.Cancers · 2026Article
- An Unsupervised Deep Learning Framework for Quantitative Breast Density Estimation from Mammograms.Journal of imaging · 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
- Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.Journal of imaging informatics in medicine · 2025Article
- Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review.Journal of imaging · 2025Review
- Classification of asymmetry in mammography via the DenseNet convolutional neural network.European journal of radiology open · 2023Review
- Comparison between two packages for pectoral muscle removal on mammographic images.La Radiologia medica · 2022Article
- Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.AJR. American journal of roentgenology · 2021Article
- Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method.Life (Basel, Switzerland) · 2021Article
- Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Spatial Correlation and Breast Cancer Risk.Biomedical physics & engineering express · 2019Article
- Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring.Journal of imaging · 2019Article
- Generalized breast density metrics.Physics in medicine and biology · 2018Article
- The role of breast tomosynthesis in a predominantly dense breast population at a tertiary breast centre: breast density assessment and diagnostic performance in comparison with MRI.European radiology · 2018Article
- Breast Tissue Organisation and its Association with Breast Cancer Risk.Breast cancer research : BCR · 2017Article
- Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad.Diagnostics (Basel, Switzerland) · 2017Review
- Quantification of masking risk in screening mammography with volumetric breast density maps.Breast cancer research and treatment · 2017Article
- Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.Annals of internal medicine · 2016Observational
- Article
- Primary Care Provider Preferences Regarding Artificial Intelligence in Point-of-Care Cancer Screening.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.
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.