Mammography

Primary diagnostic delays in mammography screening led to a greater than 10 percent higher incidence of lymph node metastasis with invasive breast cancer in comparison to women without a primary diagnostic delay, according to new research out of the Netherlands.

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In a new study comparing standard breast MRI, abbreviated breast MRI and contrast-enhanced mammography in supplemental breast cancer screening, researchers found that MRI offered a greater than 14 percent higher cancer detection rate and a nearly 39 percent higher sensitivity rate than CEM.

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Reportedly the first randomized trial to examine the impact of artificial intelligence (AI) on screening mammography, researchers found AI-aided screening led to a 20 percent increase in breast cancer detection and a 44.3 percent decrease in mammography screening workload.

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In a study of over 1.300 women with dense breasts, the combination of mammography and ultrasound had a recall rate of 11.7 percent, a specificity rate of 89.1 percent and an accuracy rate of 89.2 percent in comparison to a 21.4 percent recall rate, 79.4 percent specificity and 79.5 percent accuracy for the combination of mammography, ultrasound, and artificial intelligence (AI).

In a dataset enriched for African American women, BRCA mutation carriers and those with benign breast disease, a mammography-based deep learning model demonstrated a five-year AUC of 63 percent for predicting breast cancer in comparison to 54 percent for BI-RADS assessment.

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Catch up on the top AI-related news and research from the past month.

Ultravist is reportedly the first contrast agent to gain a specific indication for visualization of known or suspected lesions on contrast-enhanced mammography, which was recently recommended by the American College of Radiology as a supplemental imaging alternative to magnetic resonance imaging (MRI) in women with dense breasts at the age of 40 and other risk factors for breast cancer.

In multiple mammography datasets with the original radiologist-detected abnormality removed, deep learning detection of breast cancer had an average area under the curve (AUC) of 87 percent and an accuracy rate of 83 percent, according to research presented at the recent Society for Imaging Informatics in Medicine (SIIM) conference.

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In a new study involving over 400,000 women, researchers found that ultrasound screening was performed for 95.3 percent of women with dense breasts but only 21.7 percent of women with a first-degree family history of breast cancer.

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Five artificial intelligence (AI) algorithms for mammography assessment were better at predicting breast cancer risk over five years than the Breast Cancer Surveillance Consortium (BCSC) risk model, according to new retrospective research involving over 13,000 women.

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In a large retrospective study involving over 523,000 digital breast tomosynthesis (DBT) exams and over one million digital mammography (DM) exams, researchers found that DBT was associated with significantly lower recall rates but showed no advantage over DM in the diagnosis of interval or advanced breast cancer.

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In a study involving over 1,100 women diagnosed with breast cancer, researchers found that 48.7 percent of women alive or dead from other causes at a median follow-up of 11.7 years had moderately dense breasts. They also found that 46 percent of women who died from breast cancer at a median-follow-up of 5.3 years had moderately dense breasts.

Six reader studies on digital mammography revealed a pooled sensitivity rate of 80.8 percent for stand-alone artificial intelligence (AI) in comparison to 72.4 percent for radiologist assessment while seven historic cohort studies showed a 75.8 percent pooled sensitivity rate for stand-alone AI versus 72.6 percent for radiologist interpretation of digital mammography.

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The United States Preventive Services Task Force (USPSTF) has drawn praise for lowering the age threshold for initial mammography screening from 50 to 40 years of age in updated draft recommendations for breast cancer screening, but critics warn that biennial screening is not sufficient for higher-risk populations.

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While calling for a universal breast cancer risk assessment by the age of 25, the American College of Radiology (ACR) emphasized that ascertaining screening needs prior to the age of 40 is particularly important in high-risk populations such as Black women, who are 42 percent more likely to die from breast cancer in comparison to non-Hispanic White women.

Regardless of experience level, radiologists are likely to be affected by automation bias when utilizing adjunctive artificial intelligence (AI) for mammography interpretation, according to newly published research.

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