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Mammography

Mammography is a diagnostic imaging technique used to detect and evaluate breast abnormalities.
It involves the use of low-dose x-rays to capture detailed images of the breast tissue, allowing for early detection of breast cancer and other breast-related conditions.
Mammography is a crucial tool in the fight against breast cancer, as it can identify tumors and other changes in the breast before they are palpable.
The process typically involves compressing the breast between two plates to obtain the necessary images.
Mammography is recommended for routine breast cancer screening, particularly for women over the age of 40, as it has been shown to reduce breast cancer mortality rates.
However, the technique is not infallible, and false-positive and false-negative results can occur, highlighting the need for continued research and optimization of mammography protocols.
PubCompare.ai is a valuable resource that helps researchers and healthcare professionals locate the most effective mammography techniques and products by providing AI-driven comparisons of the latest literature, preprits, and patents.
This tool enhances the reproducibility and accuracy of mammography research, ultimately leading to improved breast cancer detection and patient outcomes.

Most cited protocols related to «Mammography»

1
Breast Cancer Cohort from Karolinska Hospital
We included all breast cancer patients that were operated on at the Karolinska Hospital from 1 January 1994 to 31 December 1996 (n = 524), identified from the population-based Stockholm–Gotland breast cancer registry established in 1976. Available tumor material was frozen on dry ice or in liquid nitrogen and was stored in -70°C freezers. Figure 1 shows the details of various exclusions leading to the final 159 patients for analysis. The ethical committee at the Karolinska Hospital approved this microarray expression project.
The different reasons for exclusion were not influenced by age at diagnosis (Table 1). The 231 tumors that were not analyzed using expression profiling had a lower mean diameter, had fewer mean affected lymph nodes, and had fewer individuals with recurrent disease at the end of the study period (Table 1). For those excluded for other reasons, there did not seem to be a selection based on age or stage of the disease, compared with those patients included in the study (Table 1).
The Stockholm–Gotland Breast Cancer Registry, supplemented with patient records, were examined for information on the tumor size, the number of retrieved and metastatic axillary lymph nodes, the hormonal receptor status, distant metastases, the site and date of relapse, initial therapy, therapy for possible recurrences, the date and cause of death. Tumor sections from the primary tumors from patients with array profiles were classified using Elston–Ellis grading [18 (link)] by a blinded pathologist (HN).
In the adjuvant setting tamoxifen and/or goserelin is normally used for hormonal treatment, but mostly intravenous cyclophosphamide, methotrexate and 5-fluorouracil (CMF) on days 1 and 8 was used as adjuvant chemotherapy, except in high-risk patients who were offered inclusion in the Scandinavian Breast Group 9401 study [19 (link)]. After primary therapy, patients were recommended to have regular clinical examinations and yearly mammograms, in addition to laboratory and X-ray tests guided by clinical signs and symptoms. Patients were normally followed for 5 years. Patients followed up outside the Karolinska Hospital were tracked using a unique personal identification number. There was no loss to follow-up.
The relapse site, date of relapse, relapse therapy and date of death were ascertained in May 2002. The average follow-up was 6.1 years. Cause of death was coded as death due to breast cancer (including those with distant metastases but dying from other causes), death due to other malignancies and death due to nonmalignant disorders. Through the population-based Swedish Cancer Registry, second primary malignancies were identified.
Pawitan Y., Bjöhle J., Amler L., Borg A.L., Egyhazi S., Hall P., Han X., Holmberg L., Huang F., Klaar S., Liu E.T., Miller L., Nordgren H., Ploner A., Sandelin K., Shaw P.M., Smeds J., Skoog L., Wedrén S, & Bergh J. (2005). Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Research, 7(6), R953-R964.
Publication 2005
Axilla Breast Chemotherapy, Adjuvant Cyclophosphamide Diagnosis Dry Ice Fluorouracil Freezing Goserelin Malignant Neoplasm of Breast Malignant Neoplasms Mammography Methotrexate Microarray Analysis Neoplasm Metastasis Neoplasms Nitrogen Nodes, Lymph Pathologists Patients Pharmaceutical Adjuvants Physical Examination Precancerous Conditions Radiography Recurrence Relapse Scandinavians Second Primary Cancers Tamoxifen Therapeutics
2
Measuring Mammographic Density Reliably
To assess mammographic density, the craniocaudal views of both breasts were digitized at 261 m/pixel with a Lumysis 85 laser film scanner, which covers a range of 0 to 4.0 absorbance. Film screen images were digitized and viewed on the computer screen and total breast area and total dense area were assessed using Cumulus software [16 (link)]. Percent mammographic density was calculated as absolute dense area divided by total breast area. All images were read by a single reader in two batches of mammograms approximately three years apart. Although there was high reproducibility within batch (within-person intraclass correlation coefficients ≥0.90; [17 (link)]), there was evidence of batch-to-batch variability in density measurements. Therefore, for the larger case-control dataset, we fit separate multivariable linear regression models to estimate the effect of batch on density measurements, adjusting for age, menopausal status, body mass index, and case-control status [18 (link)]. We then adjusted density measurements in the second batch by adding the coefficient for mammogram batch to the raw value to estimate the measurements that would have been obtained if the mammogram had been included in the first batch.
We used the average percent density of both breasts for our main analyses [19 (link), 20 (link)]. However, recent evidence suggests that absolute dense and non-dense areas may be independently associated with breast cancer risk [17 (link), 21 (link), 22 (link)], so we also examined these as separate outcomes in secondary analyses.
Bertrand K.A., Eliassen A.H., Hankinson S.E., Gierach G.L., Xu X., Rosner B., Ziegler R.G, & Tamimi R.M. (2012). Urinary estrogens and estrogen metabolites and mammographic density in premenopausal women. Breast cancer research and treatment, 136(1), 277-287.
Publication 2012
Breast Chest Index, Body Mass Malignant Neoplasm of Breast Mammography Menopause
3
Measuring Breast Cancer Perceptions and Behaviors
The perceived susceptibility scale, initially developed in 1997, included five items with an alpha of 0.83 and a test–retest of 0.68 (Champion & Scott, 1997 (link)). The first three susceptibility items involved the likelihood of getting breast cancer at five years, 10 years and during a lifetime. The fourth item stated that getting breast cancer was a possibility. The fifth item addressed concerns regarding getting breast cancer. The fourth and firth items were replaced with an item that reflected the risk of getting breast cancer compared to other women.
The perceived benefits component is defined as the perceived effectiveness of the action or behavior to decrease the threat—in this case, death from breast cancer. The benefits of mammography scale included four items with an internal reliability alpha of 0.65 and test–retest value of 0.40. Item wording was changed slightly based on feedback from an African American focus group.
The 1997 barrier scale had 13 items with an internal reliability alpha of 0.85 and test–retest alpha of 0.66. The current barriers scale had 19 items. Several changes were made following the focus group feedback. The 1997 item about being afraid that something was wrong was altered to reflect being afraid of finding a breast lump and not wanting to know if breast cancer were discovered. The 1997 items about difficulty with transportation and child care were changed to one item about inconvenience. A 1997 item about understanding the mammography process was deleted because it was felt that this item reflected self-efficacy. The six items that were added included: (1) forgetting to get a mammogram; (2) considering the treatment worse than the cure; (3) Fear of a mammogram causing breast cancer; (4) feeling too old to get a mammogram; (5) no need because the doctor already examines my breast; and (6) the feeling that getting a mammogram would cause breast cancer.
The self-efficacy scale, consisting of 10 items, had an internal reliability coefficient of 0.91 (Champion, Skinner, & Menon, 2005 (link)). A one-dimensional factor structure indicated all items in this scale loaded at 0.60 or above. This scale also discriminated between adherent and non-adherent women (p < .003).
The fear scale included eight items that measured emotional reaction to thinking about breast cancer. The items were found to be valid and reliable in another study that included Caucasian and African American women (Champion, Menon, Rawl, & Skinner, 2004 ). The internal consistency reliability alpha was found to be 0.91. Construct validity of this scale has been documented using factor analysis and testing of theoretical relationship (Champion et al., 2004 ).
CHAMPION V.L., MONAHAN P.O., SPRINGSTON J.K., RUSSELL K., ZOLLINGER T.W., SAYWELL RM J.R, & MARAJ M. (2008). Measuring Mammography and Breast Cancer Beliefs in African American Women. Journal of health psychology, 13(6), 827-837.
Publication 2008
African American BAD protein, human Breast Caucasoid Races Emotions Fear Malignant Neoplasm of Breast Mammography Phobia, cancer Physicians Susceptibility, Disease Woman
4
Randomized Trial of Axillary Lymph Node Dissection
The study and design end points have been described else-where.9 (link),10 (link) Briefly, after stratification based on age, hormone receptor status, and tumor size, patients with 1 or 2 sentinel nodes with metastases detected by hematoxylin and eosin stain were randomized to no further axillary-specific treatment including no axillary third-field irradiation (SLND alone group) or completion ALND (ALND group). Patients were assessed for disease recurrence with a history and physical examination every 6 months for the first 36 months and yearly thereafter. Annual mammography was required; other testing was based on individual symptoms or by investigator preference.
Follow-up was planned for 10 years. The primary study end point was overall survival, which was defined as the time from randomization until death from any cause. Disease-free survival, which was defined as the time from randomization to death or first breast cancer recurrence, was a secondary end point along with morbidity and locoregional recurrence. Locoregional recurrence was defined as a tumor in the breast or in ipsilateral axillary, internal mammary, subclavicular, or supraclavicular nodes. All other disease sites were defined as distant metastases. Secondary end points have been reported.6 (link),13 (link)
Giuliano A.E., Ballman K.V., McCall L., Beitsch P.D., Brennan M.B., Kelemen P.R., Ollila D.W., Hansen N.M., Whitworth P.W., Blumencranz P.W., Leitch A.M., Saha S., Hunt K.K, & Morrow M. (2017). Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial. JAMA, 318(10), 918-926.
Publication 2017
Axilla Breast Breast Carcinoma Eosin Hematoxylin Hormones Mammography Neoplasms Patients Physical Examination Radiotherapy Recurrence Sentinel Lymph Node
5
Neoadjuvant Anti-HER2 Therapy Trial

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Publication 2012
Biopharmaceuticals Bone Marrow Breast Breast Neoplasm Echocardiography ERBB2 protein, human Ethics Committees Heart Inflammatory Breast Carcinoma Kidney Lapatinib Malignant Neoplasm of Breast Mammography Neoplasm Metastasis Neoplasms Paclitaxel Patients Radionuclide Imaging Thoracic Surgical Procedures Trastuzumab Ultrasonography Ventricular Ejection Fraction Woman

Most recents protocols related to «Mammography»

1
Mitochondria and MAMs Isolation
Cells were collected and the resulting pellet after centrifugation (600–800 g for 5 min at RT) was resuspended in 5 ml of Starting Buffer 1 (SB1) containing 225 mM mannitol, 75 mM sucrose and 30 mM Tris–HCl, 0.1 mM EGTA, pH 7.4 and homogenized. Unbroken cells and nuclei were removed by centrifugation of the cell homogenate at 600 g for 5 min (at 4°C). The crude mitochondrial fraction (mito crude) was pelleted by centrifuging the supernatant at 7,000 g for 10 min at 4°C while the supernatant contained the ER. The crude mitochondria were resuspended in SB (225 mM mannitol, 75 mM sucrose and 30 mM Tris–HCl, pH 7.4) and after other two sequential centrifugations (7,000 g and 10,000 g for 10 min at 4°C), the obtained pellet was resuspended in 1 ml of mitochondria resuspending buffer MRB (250 mM mannitol, 5 mM HEPES and 0.5 mM EGTA), layered on top of a percoll gradient (30 and 15%) and spun down at 95,000 g for 40 min (using a Beckman ultracentrifuge, rotor SW41), to separate MAMs from the pure mitochondria (mito pure). Further ultracentrifugation (70Ti rotor, 100,000 g for 1 h at 4°C) was required to obtain the pellet of the MAMs fraction.
Sassano M.L., van Vliet A.R., Vervoort E., Van Eygen S., Van den Haute C., Pavie B., Roels J., Swinnen J.V., Spinazzi M., Moens L., Casteels K., Meyts I., Pinton P., Marchi S., Rochin L., Giordano F., Felipe-Abrio B, & Agostinis P. (2023). PERK recruits E-Syt1 at ER–mitochondria contacts for mitochondrial lipid transport and respiration. The Journal of Cell Biology, 222(3), e202206008.
Publication 2023
Buffers Cell Nucleus Cells Centrifugation Egtazic Acid G-800 HEPES Mammography Mannitol Mitochondria Mitochondrial Inheritance Mitomycin Percoll Sucrose Tromethamine Ultracentrifugation
2
Evaluating Breast Lesions with INbreast
This study used a digital breast X-ray database named INbreast to implement the proposed CAD approach. The INbreast dataset is a public database that contains more recent FFDM images. It typically has an image size of 3328 × 4084 pixels. It contains 115 patients' cases along with 410 mammograms with both craniocaudal (CC) view and a mediolateral oblique (MLO) view. Of these 115 patients, 90 had mammograms taken of both breasts, totaling 360 images, while the other 25 had only two mammograms taken each. In total, 410 mammograms were produced from 115 patients, including cases of normal, benign, and malignant breasts. 107 cases with breast lesions were used from the MLO and CC views for evaluation purposes.
Rahman H., Naik Bukht T.F., Ahmad R., Almadhor A, & Javed A.R. (2023). Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2023, 7717712.
Publication 2023
Breast Digital Radiography Mammography Patients
3
Region-Based Tumor Segmentation in Mammograms
It is a simple approach compared to other methods, as it involves dividing an image into different sections based on predetermined. Compared to others, it is a straightforward method because it entails separating an image into different sections based on predetermined criteria [58 (link)]. There are two primary kinds of region-based segmentation: (1) region splitting and merging and (2) region growing. Region growing allows the removal of a region from an image using defined criteria, such as intensity. It involves selecting a starting seed point. It is important to note that unlike region growing, region splitting and merging work on the entire image [59 ].
In the present study extracting the region of interest (ROI) involves using both thresholding and region-based techniques. The tumor in the INbreast dataset samples cites moreira2012inbreast is labeled by a white bounding box, as shown in Figure 4. For extracting ROI, the tumor region is first determined by setting a threshold value based on the white color pixels in the image. The threshold for all images is determined to be 80 after several attempts, independent of tumor size. After identifying the greatest area inside this threshold within the image, the tumor is automatically cropped. Figure 4 shows ROI extracted using threshold and region-based methods.
The method for extracting ROI can be summarized in four steps:

Thresholding the grayscale mammogram image to create a binary image.

Labelling and counting the binary image objects, then retaining only the largest one, which is the tumor, as defined by the white bounding box.

Assign the largest area within the threshold value to “1” and the rest a value of “0.”

Multiply binary image with original mammogram image for obtaining final ROI without including other parts of breast or artifacts.

Rahman H., Naik Bukht T.F., Ahmad R., Almadhor A, & Javed A.R. (2023). Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2023, 7717712.
Publication 2023
Breast Mammography Neoplasms
4
Contrast-Limited Adaptive Histogram Equalization for Image Enhancement
Image enhancement refers to increasing contrast and suppressing noise in mammogram images to assist radiologists in detecting breast abnormalities. Various image enhancement methods exist, including adaptive contrast enhancement (AHE). AHE improves the local contrast and reveals more image details, making it a helpful technique for enhancing both natural and medical images [52 (link)]. However, it may also result in considerable noise. In this paper, we utilized the contrast-limited adaptive histogram equalization (CLAHE) technique, a form of AHE, to enhance image contrast [52 (link)]. A drawback of AHE is that it can over-enhance the images due to the integration process [49 (link)]. To mitigate this issue, CLAHE is used as it limits the local histogram by setting a clip level, thus controlling contrast enhancement. Figure 3 illustrates an image enhanced by the CLAHE algorithm.
Furthermore, CLAHE algorithm steps are given as follows [53 (link)]:

Split image into equal-sized contextual regions.

Apply histogram equalization to all contextual regions.

Limit the histogram to the level of the clip.

Reallocate the clipped values in the histogram.

Obtain enhanced pixel value through histogram integration.

Rahman H., Naik Bukht T.F., Ahmad R., Almadhor A, & Javed A.R. (2023). Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2023, 7717712.
Publication 2023
Acclimatization Breast Congenital Abnormality Mammography Radiologist
5
AMS-dating of Sidon Archaeological Phases
Our samples for AMS-dating were obtained on short-lived organic material from secure and stratigraphically well-defined contexts. As secure contexts, we appraise archaeological assemblages that did not contain later archaeological material, i.e. ceramics that obviously intruded from later into earlier phases. Archaeologists involved in the excavation of multilayer settlements sites are aware of the appearance of residual material through reworking due to levelling and building activities, including the opening of pits. It was due to such activities that sparse pottery finds of Mycenaean type or origin appeared throughout the Early Iron Age stratigraphic sequence of Sidon. This applies in particular to our 14C-dated Phases C, D and E. It is not a coincidence that more than half of the Mycenaean and other Late Bronze Age pottery sherds from the site were found in just one room (Room 6), and this is the room that experienced the most extensive reworking. We also have instances of vessels that were reconstructed from sherds scattered throughout different phases. The large majority of the Early Iron Age contexts containing substantial residual ceramic material were not considered to have any value for the purpose of 14C-sampling.
To establish an absolute time-scale for Sidon Phases A-K we have at our disposal altogether 37 14C-ages on (annual-growth) olive stones and (short-lived) animal bones (Table 1). A first set of 14C-ages were processed in the year 2017 on 29 olive stones and 3 bones at the Oxford 14C-AMS laboratory (Lab Code: OxA). A second set of 14C-ages on 5 bones were processed in 2018 at the Mannheim 14C-AMS laboratory (Lab Code: MAMS). The majority of 14C-ages have standard deviations of ± 30 BP or better. According to the information provided by the laboratories, the bone collagen was extracted using weak acid dissolution, followed by ultrafiltration and separation of the fraction > 30 kD. The extracted organic carbon was then dry-frozen and burnt in an Elemental Analyser to produce CO2 which was catalysed to produce graphite. The charred olives were pre-treated using the standard ABA-method (Acid/Base/Acid; HCl/NaOH/HCl). By selectively dating olive stones (78%), whenever available, and with sampling extended to include short-lived animal bone (22%) when olive stones were not available, the idea is to avoid any kind of inbuilt ‘old wood’ effect that might be due to any perchance selection of inner tree-rings, reworked wood, or of recycled charcoal e.g. for domestic heating purposes. In consequence, potential outliers are most likely caused by stratigraphic reworking (olive stones), in addition to potential chemical alteration (bones).
Doumet-Serhal C., Gimatzidis S., Weninger B., von Rüden C, & Kopetzky K. (2023). An interdisciplinary approach to Iron Age Mediterranean chronology through combined archaeological and 14C-radiometric evidence from Sidon, Lebanon. PLOS ONE, 18(3), e0274979.
Publication 2023
Acids Animals Blood Vessel Bones Burns Calculi Carbon Charcoal Collagen Debility Freezing Graphite Iron Mammography Olea Olea europaea Trees Ultrafiltration Van der Woude syndrome

Top products related to «Mammography»

1
Selenia dimension by Hologic
Sourced in United States, United Kingdom
Selenia Dimensions is a digital breast tomosynthesis system designed for mammography. It generates 3D images of the breast, allowing for more detailed analysis of breast tissue.
2
Mammomat inspiration by Siemens
Sourced in Germany, United States
The Mammomat Inspiration is a digital mammography system designed for breast imaging. It provides high-quality imaging capabilities for the detection and diagnosis of breast cancer. The system utilizes advanced digital technology to capture detailed images of the breast, supporting healthcare professionals in their assessment and decision-making process.
3
Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
4
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
5
Selenia by Hologic
Sourced in United States
Selenia is a digital mammography system designed and manufactured by Hologic for breast cancer screening. It captures high-quality digital images of the breast to aid in the detection and diagnosis of breast cancer.
6
Senographe 2000d by GE Healthcare
Sourced in United States
The Senographe 2000D is a digital mammography system manufactured by GE Healthcare. It is designed to capture high-quality images of the breast for diagnostic and screening purposes. The system uses a solid-state digital detector to acquire images, which are then displayed on a computer monitor for review by healthcare professionals.
7
Lorad selenia by Hologic
Sourced in United States
The Lorad Selenia is a digital mammography system designed for breast imaging. It captures high-quality digital images of the breast, which can be used by healthcare professionals for diagnosis and evaluation.
8
Senographe essential by GE Healthcare
Sourced in United States, France
The Senographe Essential is a digital mammography system designed for breast imaging. It provides high-quality digital images for the detection and diagnosis of breast cancer.
9
Senographe ds by GE Healthcare
Sourced in United States
The Senographe DS is a digital mammography system designed for breast imaging. It utilizes advanced digital imaging technology to capture high-quality images of the breast for diagnostic purposes. The core function of the Senographe DS is to provide healthcare professionals with the necessary imaging data to assist in the detection and diagnosis of breast-related conditions.
10
Omnipaque 350 by GE Healthcare
Sourced in United States, China, Ireland, Italy, Germany
Omnipaque 350 is a non-ionic, water-soluble iodinated contrast medium used for radiographic procedures. It is a pharmaceutical product manufactured by GE Healthcare for diagnostic imaging applications.

More about "Mammography"

Mammography is a critical diagnostic imaging technique that uses low-dose x-rays to capture detailed images of breast tissue.
This non-invasive procedure is a crucial tool in the early detection and prevention of breast cancer, helping to identify tumors and other abnormalities before they become palpable.
The mammography process typically involves compressing the breast between two plates to obtain the necessary images.
Advances in mammography technology, such as the Selenia Dimensions, Mammomat Inspiration, and Senographe 2000D systems, have improved image quality and reduced radiation exposure, enhancing the accuracy and effectiveness of breast cancer screening.
In addition to traditional mammography, other imaging modalities like digital mammography (e.g., Senographe Essential, Senographe DS) and contrast-enhanced mammography (using agents like Omnipaque 350) are also used to provide more detailed information about breast health.
These techniques, combined with data analysis software like SAS 9.4, help healthcare professionals better interpret mammography results and develop personalized treatment plans.
While mammography is a powerful tool, it is not infallible, and false-positive and false-negative results can occur.
Ongoing research and optimization of mammography protocols, facilitated by resources like PubCompare.ai, are crucial to improving the accuracy and reproducibility of mammography findings, ultimately leading to better breast cancer detection and patient outcomes.