Trial reportPloS one2014
Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Genetic architecture of mammographic density as a risk factor for breast cancer: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Pooled it
- Maximum-likelihood estimation of glandular fraction for mammography and its effect on microcalcification detection.Physical and engineering sciences in medicine · 2025Article
- Overcoming challenges in conducting early phase breast cancer prevention trials: Bazedoxifene and conjugated estrogens vs waitlist control.Contemporary clinical trials · 2024Article
- Potential Role of Zinc Finger 365Iranian journal of medical sciences · 2023Article
- Explainable Multimedia Feature Fusion for Medical Applications.Journal of imaging · 2022Article
- Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.PloS one · 2021Article
- Subjective Versus Quantitative Methods of Assessing Breast Density.Diagnostics (Basel, Switzerland) · 2020Article
- Combined effect of volumetric breast density and body mass index on breast cancer risk.Breast cancer research and treatment · 2019Article
- Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation.Medical physics · 2019Review
- Common genetic variation and novel loci associated with volumetric mammographic density.Breast cancer research : BCR · 2018Article
- Breast Tissue Organisation and its Association with Breast Cancer Risk.Breast cancer research : BCR · 2017Article
- E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017Article
- Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.Breast cancer research : BCR · 2016Article
- Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.AJR. American journal of roentgenology · 2016Article
- Identification of two novel mammographic density loci at 6Q25.1.Breast cancer research : BCR · 2015Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.
methodsThe data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.
resultsAll three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1 × 10(-6) for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).
conclusionsOur results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.
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