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Scientific Data Mar 2023Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby...
Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Although some public mammography datasets are useful, there is still a lack of open access datasets that expand beyond the white population as well as missing biopsy confirmation or with unknown molecular subtypes. To fill this gap, we build a database containing two online breast mammographies. The dataset named by Chinese Mammography Database (CMMD) contains 3712 mammographies involved 1775 patients, which is divided into two branches. The first dataset CMMD1 contains 1026 cases (2214 mammographies) with biopsy confirmed type of benign or malignant tumors. The second dataset CMMD2 includes 1498 mammographies for 749 patients with known molecular subtypes. Our database is constructed to enrich the diversity of mammography data and promote the development of relevant fields.
Topics: Female; Humans; Biopsy; Breast Diseases; Breast Neoplasms; Mammography
PubMed: 36882402
DOI: 10.1038/s41597-023-02025-1 -
European Radiology 1997Mammography is a branch of radiology which could benefit greatly from the assimilation of digital imaging technologies. Computerized enhancement techniques could be used... (Review)
Review
Mammography is a branch of radiology which could benefit greatly from the assimilation of digital imaging technologies. Computerized enhancement techniques could be used to ensure optimum presentation of all clinical images. Beyond this it will facilitate powerful new clinical resources such as computer-assisted diagnosis, tele-mammography, plus digital image management and archiving. An essential precursor to all these advances is the availability of appropriate direct digital mammography (DDM) image-acquisition system(s) to capture high-quality breast X-ray image data at the outset. The only practical DDM image-acquisition system currently available is (photo-stimulable phosphor) computed radiography. Modern computed mammography (CM) uses similar radiation doses to the patient and produces equivalent, albeit different, image quality to screen-film mammography. Computed mammography offers superior rendition of the skin edge and sub-cutaneous tissue and dense parenchyma, while ensuring equivalent micro-calcification detectability. Meanwhile, a variety of new technical approaches to DDM are under active investigation and/or development which promise to supercede film-based mammography. These new (second generation) DDM technologies promise the radiologist superior image quality combined with significant dose savings compared with contemporary imaging systems. In this review we describe and compare the physical and clinical characteristics of CM and the various emerging DDM image-acquisition technologies.
Topics: Female; Humans; Mammography; Radiographic Image Enhancement
PubMed: 9228110
DOI: 10.1007/s003300050228 -
AJR. American Journal of Roentgenology Dec 2011Early detection of breast cancer is directly related to the radiologist's ability to detect abnormalities visible only on mammograms. Artifacts on mammograms reduce... (Review)
Review
OBJECTIVE
Early detection of breast cancer is directly related to the radiologist's ability to detect abnormalities visible only on mammograms. Artifacts on mammograms reduce image quality and may present clinical and technical difficulties for the radiologist, mammography technologist, medical physicist, and equipment service personnel.
CONCLUSION
In this article, we will illustrate the appearance of artifacts in full field digital mammography, review the causes of these artifacts, and discuss methods to eliminate artifacts in digital mammography.
Topics: Algorithms; Artifacts; Breast Neoplasms; Early Diagnosis; Female; Humans; Mammography; Radiographic Image Interpretation, Computer-Assisted
PubMed: 22109316
DOI: 10.2214/AJR.10.7246 -
Health Devices Jan 1989We evaluated eight mammorgraphy units from five manufacturers, basing our tests on the units' screen-film imaging capability. Our ratings were based primarily on the...
We evaluated eight mammorgraphy units from five manufacturers, basing our tests on the units' screen-film imaging capability. Our ratings were based primarily on the units' ability to safely and consistently produce acceptable images with minimal patient dose. One unit is rated Acceptable-Preferred, two units are rated Acceptable, two units are rated Acceptable but not recommended for magnification applications, and three units are rated Conditionally Acceptable because of poor automatic exposure control performance.
Topics: Aged; Breast Neoplasms; Equipment Design; Equipment Safety; Ergonomics; Evaluation Studies as Topic; Female; Humans; Mammography; Reproducibility of Results; Technology Assessment, Biomedical; Xeroradiography
PubMed: 2634645
DOI: No ID Found -
European Radiology Nov 2004This paper reviews the different techniques available and competing for full-field digital mammography. The detectors are described in their principles: photostimulable... (Review)
Review
This paper reviews the different techniques available and competing for full-field digital mammography. The detectors are described in their principles: photostimulable storage phosphor plates inserted as a cassette in a conventional mammography unit, dedicated active matrix detectors (i.e., flat-panel, thin-film transistor-based detectors) and scanning systems, using indirect and direct X-ray conversion. The main parameters that characterize the performances of the current systems and influence the quality of digital images are briefly explained: spatial resolution, detective quantum efficiency and modulation transfer function. Overall performances are often the result of compromises in the choice of technology.
Topics: Equipment Design; Humans; Mammography; Radiographic Image Enhancement
PubMed: 15480692
DOI: 10.1007/s00330-004-2446-6 -
RoFo : Fortschritte Auf Dem Gebiete Der... Feb 1994
Review
Topics: Breast Neoplasms; Diagnosis, Differential; Female; Humans; Image Interpretation, Computer-Assisted; Mammography; Quality Assurance, Health Care; Radiographic Image Enhancement; Stereotaxic Techniques
PubMed: 8312504
DOI: 10.1055/s-2008-1032385 -
European Heart Journal. Cardiovascular... Mar 2024Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for...
AIMS
Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events.
METHODS AND RESULTS
We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667-0.830); specificity, 0.652 (95% CI, 0.614-0.710); AUROC, 0.761 (95% CI, 0.742-0.780); and accuracy, 70.8% (95% CI, 68.8-72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762-0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712-0.761).
CONCLUSION
Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes.
Topics: Adult; Female; Humans; Middle Aged; Breast Neoplasms; Cardiovascular Diseases; Coronary Artery Disease; Deep Learning; Mammography
PubMed: 37988168
DOI: 10.1093/ehjci/jead307 -
Radiology Jan 1999To measure directly the grid performance of mammography units for the range of breast thicknesses and x-ray tube potentials encountered in clinical practice.
PURPOSE
To measure directly the grid performance of mammography units for the range of breast thicknesses and x-ray tube potentials encountered in clinical practice.
MATERIALS AND METHODS
Contrast improvement factors and Bucky factors were determined for four mammographic units as a function of x-ray tube potential (25, 30, and 35 kVp), phantom thickness (2, 4, and 8 cm) and, on one unit, three target-filter combinations. Three units used a linear grid; one, a cellular grid. Two methods were used for nongrid measurements.
RESULTS
For all units tested, contrast improvement factor increased with increased phantom thickness and with increased kilovolt peak level for the 8-cm-thick phantom and changed little with kilovolt peak level for 2- and 4-cm-thick phantoms. At 25 and 30 kVp, contrast improvement factor performance with the linear grids was comparable; with the cellular grid, it was 5%-10% higher. In all cases, the Bucky factor increased with increased phantom thickness and decreased with increased tube potential.
CONCLUSION
Differences in grid performance exist. At 25 and 30 kVp, the cellular grid exhibited superior contrast improvement factor performance, whereas one of the linear grids exhibited superior Bucky factor performance. Measured contrast improvement and Bucky factors are dependent on nongrid technique. Cassette tunnels introduce scatter and should not be used with nongrid or magnification techniques.
Topics: Humans; Mammography; Phantoms, Imaging
PubMed: 9885613
DOI: 10.1148/radiology.210.1.r99dc35227 -
The Lancet. Oncology Aug 2018
Topics: Breast Neoplasms; Female; Humans; Mammography; Patient Positioning
PubMed: 30700373
DOI: 10.1016/S1470-2045(18)30489-3 -
Nihon Hoshasen Gijutsu Gakkai Zasshi 2012
Topics: Female; Humans; Mammography; Quality Control
PubMed: 22449914
DOI: 10.6009/jjrt.2012_jsrt_68.3.347