ReviewBreast cancer research : BCR2016
Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.
Review in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.Breast cancer research : BCR · 2022Pooled it
- 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
- Generalizability of an AI-based mammogram risk score (MRS) for breast cancer among diverse populations of women.Science advances · 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
- 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
- Investigating the relationship between breast cancer risk factors and an AI-generated mammographic texture feature in the Nurses' Health Study II.NPJ breast cancer · 2025Article
- Machine learning-based classification of histological subtypes of invasive breast cancer using MRI contralateral breast texture features.Scientific reports · 2025Article
- Associations of early-adulthood adiposity with breast parenchymal tissue complexity within premenopausal women.Breast cancer research : BCR · 2025Article
- Comparison of Digital Breast Tomosynthesis and Mammography-based Radiomics for Breast Cancer Risk Assessment: Case-Control Study.Radiology. Imaging cancer · 2025Article
- Validation of a Dynamic Risk Prediction Model Incorporating Prior Mammograms in a Diverse Population.JAMA network open · 2025Article
- Radiomic Parenchymal Phenotypes of Breast Texture from Mammography and Association with Risk of Breast Cancer.Radiology · 2025Article
- Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.JCO clinical cancer informatics · 2024Article
- Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening.Diagnostics (Basel, Switzerland) · 2024Article
- Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogram.The British journal of radiology · 2024Article
- Saliency of breast lesions in breast cancer detection using artificial intelligence.Scientific reports · 2023Article
- Malmö Breast ImaginG database: objectives and development.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Breast density analysis of digital breast tomosynthesis.Scientific reports · 2023Article
- Causal relationships between breast cancer risk factors based on mammographic features.Breast cancer research : BCR · 2023Article
- Long Prehensile Protrusions Can Facilitate Cancer Cell Invasion through the Basement Membrane.Cells · 2023Article
- Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect.Journal of medical imaging (Bellingham, Wash.) · 2023Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation. MAIN TEXT: The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.
conclusionsThe objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.
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