The added value of digital breast tomosynthesis in improving diagnostic performance of BI-RADS categorization of mammographically indeterminate breast lesions

This study aimed to evaluate the added value of digital breast tomosynthesis (DBT) to BI-RADS classification in categorization of indeterminate breast lesions after digital mammography (DM) as an initial approach.Methods and resultsWe prospectively evaluated 296 women with BI-RADS indeterminate breast lesions (BI-RADS 0, 3, and 4) by DM between January 2018 and October 2019. All patients underwent DBT. Two radiologists evaluated lesions and assigned a BI-RADS category to each lesion according to BI-RADS lexicon 2013 classification using DM, DBT, and combined DM and DBT. The results were compared in terms of main radiological features, diagnostic performance, and BI-RADS classification using histopathology as the reference standard. A total of 355 lesions were detected on DBT and 318 lesions on DM. Thirty-seven lesions were detected by DBT and not seen by DM. The final diagnoses of 355 lesions were 58.3% benign and 41.7% malignant. In comparison to DM, DBT produced 31.5% upgrading and 35.2% downgrading of BI-RADS scoring of breast lesions. DBT reduced number of BI-RADS 3 and 4, compared to DM. All upgraded BI-RADS 4 were malignant. The combination of DBT and DM significantly increased the performance of BI-RADS in the diagnosis of indeterminate breast lesions versus DM or DBT alone (p
Source: Insights into Imaging - Category: Radiology Source Type: research

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Conclusion: Worst indicators among women receiving BF reflect social inequalities inherent in this most vulnerable group. The study also shows that BF is being targeted at the most vulnerabl e women.
Source: Revista Brasileira de Epidemiologia - Category: Epidemiology Source Type: research
AGFA Digital Radiography X- Ray system DR 800 with MUSICA Dynamic, Type number: 6010/200 - Product Usage: DR 800: Agfa s DR 800 system is indicated for performing dynamic imaging examinations (fluoroscopy and/or rapid sequence) of the following anatomies/procedures: positioning fluoroscopy procedures, gastro-intestinal examinations, urogenital tract examinations, and angiography. It is intended to replace fluoroscopic images obtained through image intensifier technology. In addition, the system is intended for project radiography of all body parts. The DR 800 is not intended for mammography applications
Source: Medical Device Recalls - Category: Medical Devices Source Type: alerts
Conclusion: PET with 11C-vorozole is a useful technique for measuring aromatase expression in individual breast lesions, enabling noninvasive quantitative measurement of baseline and posttreatment aromatase availability in primary tumors and metastatic lesions.
Source: Journal of Nuclear Medicine - Category: Nuclear Medicine Authors: Tags: Clinical Source Type: research
AbstractBackgroundCancer screening is chiefly performed by primary care providers (PCPs) who rely on organizational screening guidelines. These guidelines provide evidence-based recommendations; however, they are often without unanimity leading to divergent screening recommendations.ObjectiveDue to the high incidence of breast cancer, the availability of screening methods, and the presence of multiple incongruent guideline recommendations, we sought to understand breast cancer screening practices in Wisconsin to identify patterns that would allow us to improve evidence-based screening adherence.MethodsA 46-question survey ...
Source: Journal of General Internal Medicine - Category: Internal Medicine Source Type: research
Medical imaging saves millions of lives each year, helping doctors detect and diagnose a wide range of diseases, from cancer and appendicitis to stroke and heart disease. Because non-invasive early disease detection saves so many lives, scientific investment continues to increase. Artifical intelligence (AI) has the potential to revolutionize the medical imaging industry by sifting through mountains of scans quickly and offering providers and patients with life-changing insights into a variety of diseases, injuries, and conditions that may be hard to detect without the supplemental technology. Images are the largest source...
Source: MDDI - Category: Medical Devices Authors: Tags: Imaging Source Type: news
Women under the age of 40 are more likely than older patients to have breast...Read more on AuntMinnie.comRelated Reading: More young women are developing breast cancer Breast cancer brings financial difficulty to young women Young women at risk of breast cancer need screening Women under 40 benefit from mammography, too DBT improves breast cancer screening in young women
Source: Headlines - Category: Radiology Source Type: news
Conclusion: This study highlights the awareness needs by the women and application of extensive strategies to increase the acceptance of cancer screening programs.
Source: Indian Journal of Community Medicine - Category: International Medicine & Public Health Authors: Source Type: research
Publication date: June 2020Source: European Journal of Surgical Oncology, Volume 46, Issue 6Author(s): Lorraine Kalra, April Covington, Michelle Allchurch
Source: European Journal of Surgical Oncology (EJSO) - Category: Surgery Source Type: research
Publication date: June 2020Source: European Journal of Surgical Oncology, Volume 46, Issue 6Author(s): Carmen Francis, Anna Powell-Chandler, Asmaa Al-allak, Gary Osborn
Source: European Journal of Surgical Oncology (EJSO) - Category: Surgery Source Type: research
Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural netw...
Source: IEE Transactions on Medical Imaging - Category: Biomedical Engineering Source Type: research
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