Using artificial intelligence to measure breast density

An artificial intelligence (AI) algorithm measures breast density at the level of an experienced mammographer.

Dr Constance Lehman and colleagues from Massachusetts General Hospital (MGH) in Boston collaborated with Massachusetts Institute of Technology Professor Regina Barzilay and her team to develop an algorithm that can automatically measure breast density. 

"We're dependent on human qualitative assessment of breast density, and that approach has significant flaws," said Constance Lehman, lead author of a new study published in Radiology.

"We need a more accurate tool."

The team used tens of thousands of high-quality digital mammograms from MGH to train and test the algorithm before implementing it in routine clinical practice. Eight radiologists then reviewed 10,763 mammograms that the model had determined were either dense or non-dense tissue.

The interpreting radiologist accepted the algorithm's assessment in 10,149 of the mammograms, or 94%. Dr Lehman noted that the 94% agreement rate between the radiologists and the algorithm does not necessarily mean the machine was wrong in 6% of the cases. Reader variability could affect the disagreement because radiologists visually assess breast density, which is subjective and qualitative.

"We were thrilled with the results," Dr Lehman said. "Now at Mass General, the deep learning algorithm processes all our screening mammograms and provides density, which is either accepted or rejected by the radiologists."

"The study results show that the algorithm worked remarkably well," Dr Barzilay added. "But what's more important is that it is being used every day to measure breast density in mammograms at a major hospital."

The system has been in continuous operation at MGH since January and has processed approximately 16,000 images, according to Dr Barzilay.

Dr Lehman attributed the successful clinical implementation of the AI model to two components: the availability of high-quality, annotated data evaluated by expert radiologists and the collaborative efforts of experienced, accomplished medical and computer science professionals.

"We have to have radiologists and other physicians who understand the pressing needs of our patients and can partner with computer scientists who are experts in AI," she said. "That is the collaboration that is going to move the field forward."

The algorithm has the potential to standardise and automate routine breast density assessment, the researchers said. "We're teaching the machine to directly predict cancer risk even before the radiologist will see any cancer," Dr Barzilay said. "The best time to control the disease is when it starts."

Image caption: Test set assessment. Comparison of the original interpreting radiologist assessment with the deep learning (DL) model assessment for (a) binary and (c) four-way mammographic breast density classification. (b, d) Corresponding examples of mammograms with concordant and discordant assessments by the radiologist and with the DL model. Image credit: Radiological Society of North America.