ArticleMedical physics2016
Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.
Article in Medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Association between Participant Characteristics and Breast Parenchymal Texture Variation among Postmenopausal Women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Development and validation of a multimodality radiomics-based nomogram for predicting HER2 expression status in invasive breast cancer.Frontiers in oncology · 2026Article
- Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Associations of early-adulthood adiposity with breast parenchymal tissue complexity within premenopausal women.Breast cancer research : BCR · 2025Article
- Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
- Radiomics, deep learning and early diagnosis in oncology.Emerging topics in life sciences · 2021Review
- Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.Cancers · 2021Article
- Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography.Insights into imaging · 2021Article
- A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis.Radiology. Artificial intelligence · 2021Article
- Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.Radiology · 2020Article
- The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2020Article
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.Radiology · 2019Review
- Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation.Scientific reports · 2018Article
- Density and tailored breast cancer screening: practice and prediction - an overview.Acta radiologica open · 2018Article
- Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.Academic radiology · 2018Article
- Comparisons of the Computed Tomographic Scan and Panoramic Radiography Before Mandibular Third Molar Extraction Surgery.Medical science monitor : international medical journal of experimental and clinical research · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
purposeWith raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations.
methodsThe authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman- and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (r
resultsAll density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, p
conclusionsDifferences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations.
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