Breast Imaging

Breast imaging includes imaging modalities used for breast cancer screenings and planning therapy once cancer is detected. Mammography is the primary modality used. Mammogram technology is moving from 2D full-field digital mammography (FFDM) to breast tomosynthesis, or 3D mammography, which helps reduce false positive exams by allowing radiologists to look through the layers of tissue. Overlapping areas of dense breast tissue on 2D mammograms appear similar to cancers and 3D tomo helps determine if suspect areas are cancer or not. About 50% of women have dense breast tissue, which appears white on mammograms, the same as cancers, making diagnosis difficult. Radiologists use the Breast Imaging Reporting and Data System (BI-RADS) scoring system to define the density of breast tissue. Many states now require patients to be notified if they have dense breasts so they understand their mammograms might be suboptimal and they should use supplemental imaging that can see through the dense areas. This includes tomosythesis, breast ultrasound, automated breast ultrasound (ABUS), breast MRI, contrast enhanced mammography and nuclear imaging, including positron emission mammography (PEM).

 RBMA Board member Kit Crancer outlines the key legislation in radiology to watch.

Pending radiology bills in Congress and predicting the Trump administration's impact on imaging

Kit Crancer, RBMA board member, speaks with Radiology Business about key legislative developments on the Hill that will affect the specialty. 

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AI prevents mammography positioning errors before exposure

And it can spot positioning errors in less than two seconds, new research suggests.

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How does breast density affect AI accuracy?

AI has shown great promise for improving early detection of breast cancer, but many algorithms are hindered by a lack of training on diverse datasets. 

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FDA clears AI software that ups cancer detection in dense breasts by 50%

Women's imaging experts believe the new upgrade could “dramatically improve breast cancer detection.” 

AI model spots missed breast cancers on MRI

AI model spots up to 30% of breast cancers missed on MRI

Re-evaluation by the second look algorithm could result in a cancer diagnosis up to one year earlier, especially for high risk disease.

Radiologist Dana Ataya, MD, writes song to clear up confusion on breast cancer screening recs.

Singing radiologist pens song to clear up confusion about breast cancer screening recs

Dana Ataya, MD, is backed by Moffitt Cancer Center's band, The ReMissions.

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Breast cancer only visible via MRI is often less aggressive

The heightened sensitivity of MRI is beneficial in that it allows for earlier detection of cancers, but it also presents challenges for providers tasked with managing patients’ care plans.

MedCognetics CogNet AI-MT technology is the first embedded AI cancer detection system built into the mammography system to eliminate eliminates latency and delivering immediate, high-quality image analysis and can help prioritize exams in the worklist

AI loaded onto mammography systems can flag possible cancers in real time to speed workflows

A new AI solution offers complete mammography analysis on the imaging system, in the radiology workflow, to reduce the wait time for results. 

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CCTA is being utilized more and more for the diagnosis and management of suspected coronary artery disease. An international group of specialists shared their perspective on this ongoing trend.

The new technology shows early potential to make a significant impact on imaging workflows and patient care. 

Richard Heller III, MD, RSNA board member and senior VP of policy at Radiology Partners, offers an overview of policies in Congress that are directly impacting imaging.