AI May Improve Efficiency and Accuracy of Digital Breast Tomosynthesis
Artificial intelligence (AI) may improve the efficiency and accuracy of an advanced imaging technology used to screen for breast cancer, according to a study published by Conant et al in Radiology: Artificial Intelligence.
Digital breast tomosynthesis is an advanced method for cancer detection in which an x-ray arm sweeps over the breast, taking multiple images in a matter of seconds. Research has shown that digital breast tomosynthesis improves cancer detection and reduces false-positive recalls compared to screening with digital mammography alone. However, digital breast tomosynthesis can take almost twice as long to interpret as digital mammography, due to the time it takes for the radiologist to scroll through all the images. This increased time is likely to be more consequential as digital breast tomosynthesis increasingly becomes the standard of care for mammographic imaging.
Methods
In the study, researchers developed a deep learning system—a type of AI that can mine vast amounts of data to find subtle patterns beyond human recognition. They trained the AI system on large digital breast tomosynthesis datasets to identify suspicious findings in the images.
After developing and training the system, the researchers tested its performance by having 24 radiologists—including 13 breast subspecialists—each read 260 digital breast tomosynthesis examinations with and without AI assistance. The examinations included 65 cancer cases.
Results
Use of AI was associated with improved accuracy and shorter reading times.
Sensitivity increased from 77% without AI to 85% with AI. Specificity increased from 62.7% without AI to 69.6% with AI. The recall rate for noncancers decreased from 38% without AI to 30.9% with AI. On average, reading time decreased from just over 64 seconds without AI to 30.4 seconds with AI. Radiologist performance, measured by mean area under the curve, increased from 0.795 without AI to 0.852 with AI.
“Overall, readers were able to increase their sensitivity by 8%, lower their recall rate by 7%, and cut their reading time in half when using AI concurrently while reading digital breast tomosynthesis cases compared to reading without using AI,” said study lead author Emily F. Conant, MD, Professor and Chief of Breast Imaging, Department of Radiology, Perelman School of Medicine at the University of Pennsylvania.
“We know that digital breast tomosynthesis imaging increases cancer detection and lowers recall rate when added to 2-D mammography and even further improvement in these key metrics is clinically very important,” said Dr. Conant. “…Since adding digital breast tomosynthesis to the 2-D mammogram approximately doubles radiologist reading time, the concurrent use of AI with digital breast tomosynthesis increases cancer detection and may bring reading times back to about the time it takes to read digital mammography–alone exams.”
The researchers expect the deep learning approach to improve as it is exposed to larger datasets, making its potential impact on patient care even more significant.
“The results of this study suggest that both improved efficiency and accuracy could be achieved in clinical practice using an effective AI system,” concluded Dr. Conant.
Disclosure: For full disclosures of the study authors, visit pubs.rsna.org.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
