ArticleBreast cancer research : BCR2016
Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.
Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled 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
- 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
- Radiomics-based mammographic abnormality identification via radiologist annotations.BJR artificial intelligence · 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
- The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.Scientific reports · 2025Article
- Association between Inflammatory Dietary Pattern and Mammographic Features.The Journal of nutrition · 2024Article
- Plasma C-peptide mammographic features and risk of breast cancer.NPJ breast cancer · 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
- Associations of stem cell markers CD44, CD24 and ALDH1A1 with mammographic breast density in women with benign breast biopsies.British journal of cancer · 2024Article
- Imaging for local recurrence of breast cancer.Journal of cancer research and clinical oncology · 2024Article
- A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.Scientific reports · 2024Article
- A genome-wide association study of mammographic texture variation.Breast cancer research : BCR · 2022Article
- Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.Cancers · 2021Article
- Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram.Radiological physics and technology · 2021Article
- Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study.NPJ breast cancer · 2021Article
- Personalized early detection and prevention of breast cancer: ENVISION consensus statement.Nature reviews. Clinical oncology · 2020Review
- A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations.European radiology · 2019Article
- Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.Medical physics · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSeveral studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.
methodsThis study combines five case-control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.
resultsOf the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH_10 [corrected] and FD_TH_15) [corrected] were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH_60 to FD_TH_85) [corrected] were associated with a decreased risk. Increasing the FD_TH_75 [corrected] and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased [corrected] risk of breast cancer. For example, 1 standard deviation increase of FD_TH_75 [corrected] was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79-0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.
conclusionMammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.
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