Breast density reflects the amount of fibroglandular tissue in the breast commonly seen on mammograms. High breast density is an independent breast cancer risk factor, and its masking effect of underlying lesions. Now, an artificial intelligence (AI) tool can accurately and consistently classify breast density on mammograms.

In clinical practice, breast density is visually assessed on two-view mammograms, most commonly with the American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) four-category scale, ranging from Category A for almost entirely fatty breasts to Category D for extremely dense. The system has limitations, as visual classification is prone to inter- and intra-observer variability. To overcome this variability, researchers from the Centro Diagnostico Italiano (Milan, Italy) developed software for breast density classification based on deep learning with convolutional neural networks, a sophisticated type of AI able to discern subtle patterns in images beyond the capabilities of the human eye.

The researchers trained the software, known as TRACE4BDensity, under the supervision of seven experienced radiologists who independently visually assessed 760 mammographic images. External validation of the tool was performed by the three radiologists closest to the consensus on a dataset of 384 mammographic images obtained from a different center. TRACE4BDensity showed 89% accuracy in distinguishing between low density (BI-RADS categories A and B) and high density (BI-RADS categories C and D) breast tissue, with an agreement of 90% between the tool and the three readers. All disagreements were in adjacent BI-RADS categories. According to the researchers, such a tool would be particularly valuable, as breast cancer screening becomes more personalized, with density assessment accounting for one important factor in risk stratification. The researchers plan additional studies to better understand the full capabilities of the software.

“The particular value of this tool is the possibility to overcome the suboptimal reproducibility of visual human density classification that limits its practical usability,” said study co-author Sergio Papa, MD, from the Centro Diagnostico Italiano in Milan. “To have a robust tool that proposes the density assignment in a standardized fashion may help a lot in decision making.

"A tool such as TRACE4BDensity can help us advise women with dense breasts to have, after a negative mammogram, supplemental screening with ultrasound, MRI or contrast-enhanced mammography,” said study co-author Francesco Sardanelli, MD, from the Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Donato. 

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