Mammography

In a retrospective study involving nearly 119,000 women, researchers found that implementation of AI into mammography screening increased the positive predictive value by 11 percent, increased small cancer detection by 8.3 percent and reduced reading workload by approximately 33 percent.

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Researchers also noted that mammography-based AI software was associated with over a threefold higher likelihood of false-positive risk scores in patients 61 to 70 years of age in comparison to women 51 to 60 years of age.

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In recognition of National Women’s Health Month, Dana Bonaminio, MD, Amy Patel, MD, and Stacy Smith-Foley, MD, shared their thoughts and perspectives on the recently updated breast cancer screening recommendations from the United States Preventive Services Task Force (USPSTF).

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In a recent lecture at the 2024 ARRS Annual Meeting, Jordana Phillips, MD, discussed the role of contrast-enhanced mammography in staging breast cancer, evaluating response to neoadjuvant chemotherapy and recalls from screening.

In a recent interview, Stacy Smith-Foley, M.D., shared her perspective on the shortcomings of the recently issued breast cancer screening recommendations from the United States Preventive Services Task Force (USPSTF).

The presence of fat-enlarged axillary lymph nodes on mammography screening exams is associated with a fourfold higher risk of type 2 diabetes and a 2.6-fold higher risk for cardiovascular disease, according to new research presented at the ARRS Annual Meeting.

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For over 1,200 women with a previous history of breast cancer, interim findings from a prospective trial suggest that adding contrast-enhanced mammography (CEM) to digital breast tomosynthesis (DBT) enhances detection of second malignancies with a limited increase in recalls.

The 10-study meta-analysis demonstrated that contrast-enhanced mammography has a 95 percent sensitivity rate and an 81 percent specificity rate for diagnosing suspicious lesions in women with dense breasts.

In finalized updates to breast cancer screening recommendations, the United States Preventive Services Task Force (USPSTF) advocates biennial mammography screening for women 40 to 74 years of age and notes insufficient evidence for the use of supplemental MRI in women with dense breasts.

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One of the recommendations from the European Society of Breast Imaging (EUSOBI) is annual breast MRI exams starting at 25 years of age for women deemed to be at high risk for breast cancer.

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Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.

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Patients with Medicaid or means-tested insurance were over 27 percent more likely to miss mammography appointments, and only 65 percent of women with three of more adverse social determinants of health had a mammography exam in a two-year period covering 2020 and 2021, according to new research and a report from the CDC.

Emerging research suggests that an artificial intelligence (AI) score of 75 or greater for mammography abnormalities more than doubles the likelihood of invasive upgrade of ductal carcinoma in situ (DCIS) diagnosed with percutaneous biopsy.

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In addition to full-field digital mammography and titanium contrast-enhanced mammography capabilities, the Mammomat B.brilliant system also offers ergonomic, workflow and patient comfort benefits.

An emerging artificial intelligence (AI) model demonstrated more than 12 percent higher specificity and reduced image reading time by nearly six seconds in comparison to unassisted radiologist interpretation of digital breast tomosynthesis (DBT) images.

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A proposed alternative to a previously validated deep learning neural network for assessing short-term breast cancer risk, the emerging AsymMirai deep learning mammography-based model showed comparable breast cancer risk prediction with an emphasis on bilateral dissimilarity, according to new research.

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